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Adaptive Encoding for Constrained Video Delivery in HEVC, VP9, AV1 and VVC Compression Standards and Adaptation to Video Content

The dissertation proposes the use of a multi-objective optimization framework for designing and selecting among enhanced GOP configurations in video compression standards. The proposed methods achieve fine optimization over a set of general modes that include: (i) maximum video quality, (ii) minimum bitrate, (iii) maximum encoding rate (previously minimum encoding time mode) and (iv) can be shown to improve upon the YouTube/Netflix default encoder mode settings over a set of opposing constraints to guarantee satisfactory performance. The dissertation describes the implementation of a codec-agnostic approach using different video coding standards (x265, VP9, AV1) on a wide range of videos derived from different video datasets. The results demonstrate that the optimal encoding parameters obtained from the Pareto front space can provide significant bandwidth savings without sacrificing video quality. This is achieved by the use of effective regression models that allow for the selection of video encoding settings that are jointly optimal in the encoding time, bitrate, and video quality space. The dissertation applies the proposed methods to x265, VP9, AV1 and using new GOP configurations in x265, delivering over 40% of the optimal encodings in two standard reference videos.


Video Streaming Industry & CODEC Wars
The deployment of effective video coding standards in 5G networks aims to address the rapid growth of network traffic and bandwidth-hungry applications. Additionally, video streaming dominates the delivery of video content. Video-On-Demand (VOD) and Video streaming applications worldwide are experiencing an exponential growth with applications such as video based learning, adaptive medical video communications [5][6][7], mobile gaming and AR/VR. According to Cisco [8], global IP video traffic will account for 82% of the internet traffic in 2020 which is significantly higher than 70% back in 2015. Our everyday life is surrounded by devices connected to the internet and with so many apps, we are increasing the internet traffic with videos. Over the years, YouTube has become the major source of video traffic accounting for a significant portion of the Internet traffic followed by the streaming providers (e.g., Amazon, Disney+, Hulu and Netflix).
Limiting the pre-encoded formats to a fixed set of combinations often may or may not provide the best quality for users, since user constraints keep changing all the time. With the evolution of new codec standards and the encoder configuration options growing exponentially, the challenge for streaming video providers is not only to come up with the optimal set of pre-encoding configurations that best suit user profiles, but also to choose the "one" optimal video encoded format that gives the best possible quality for a specific user (compute power, bandwidth, display resolution, network delay). With 4K becoming standard and now 8K and even higher resolutions on the horizon, the increase in higher quality video, along with the need for such a real-time capable, resource optimizing video control system is growing more than ever before. This optimized video delivery for best quality problem applies to enterprises, consumers and government users alike.
Apart from higher consumption of videos on a daily basis due to regular streaming, video content providers also promise that their videos are always of higher quality. For example, YouTube [4] encodes 480p video @2.5Mbit/s, 720p @5Mbit/s and 1080p @8Mbit/s which are rather high values. Netflix, Facebook and Apple use their own conservative encoding-bitrate ladder settings. Even though streaming providers like Amazon Prime or Netflix have their own streaming techniques that aim to deliver higher video quality, we as users/consumers have always noted that they lack significant drop in visual quality. The quality of experience really matters when it comes to streaming VOD where the drop in bandwidth is felt directly by the consumer. So, an efficient system would be to offer higher or an acceptable quality even when there is a sudden drop in bandwidth and to sustain the quality throughout the video. Such systems can be built only after a thorough understanding of the video encoding pipeline with better optimization and control. To understand the bitrate constraints on the problem, typical recommendations by Netflix are given in Fig 1.1.
H.264/AVC has been widely adopted as the default video encoding standard as MP4 container format is the most widely used extension. On the other hand, Google's VP9 codec is deployed in YouTube and the new AV1 codec from Alliance for Open Media (AOM) is used in YouTube and other streaming platforms very However, it is not only a matter of royalty issues as there are other bottlenecks in video streaming like video buffering, frames frozen/stalled, and latency issues because of insufficient bandwidth which can happen during peak traffic hours and emergency scenarios [9,10]. Currently, at the time of this thesis we have COVID-19 [11] and there is a huge number of people working from home, students taking classes online and much more. All these situations have led to a crisis where the streamed videos will have frequent buffering, stalled frames or rendered with pixellations which is very visual and results in a direct impact on the user's overall satisfaction of the video quality being delivered. Amazon Prime/Netflix/YouTube are definitely not going to be happy when the video is of lower quality with artifacts. A possible solution is to adapt video encoding based on content and/or user provided constraints.

Motivation
The primary motivation of this dissertation is to develop a segment based encoding approach which can be applied to any encoding standard with Video-On-Demand (VOD) and streaming as applications. Prior work with DRASTIC [17] was done with a focus on specialized hardware architectures developed specifically for MJPEG, H.264/AVC, H.265/HEVC standards and a software based approach was extended to HEVC Intra coding. In [18], a joint optimization methodology was taken to study the CU depths and Intra coding together to achieve precise control and modeling at a frame level using RDO budget constraints.

Thesis statement
The thesis of the dissertation is that a multi-objective based approach can provide optimal video encodings for video delivery applications. This dissertation has developed methods to build models that can provide optimal encoding parameters across standards and can support adaptive encoding based on dynamic constraints.
This research heavily focuses on the development of Group of Picture (GOP) level control with newer GOP structures for x265/HEVC, VP9, SVT-AV1 Video Coding Standards with applications in optimal encoding, adaptive encoding using Camera activity classification and GOP level adaptation for VP9 and AV1 encoding standards. The approach uses Pareto based segment modeling and predicts the optimal encoding parameters for the next segment within a video subject to dynamic constraints.

Contributions
This dissertation demonstrates the use of Segment based encoding with efficient use of encoding parameterization and joint-optimization of rate, quality and encoding rate on software configurations available to the codecs. All of these contributions came through a DRASTIC optimization framework. • Segment based encoding with x265 and Local Pareto Models The Pareto front is used to build a regression model and uses it within video segments to predict encoding parameters along the Pareto front. This approach eliminates the need for re-encoding. VMAF based model fitting was done from the Pareto front.

AV1 Codec
The Pareto front is used to build a regression model and use it within video segments to predict encoding parameters for VP9, AOM/SVT-AV1.
• Subjective Video quality assessment This dissertation provides both subjective and objective Video quality assessment for x265, VP9, SVT-AV1 encoders. Thirty two human subjects were shown different videos encoded with different video quality levels and asked to score them. From the tests, VMAF metric proved AV1 as the winner in the perceptual quality test.
• VVC Encoding Standard & BD-PSNR and BD-VMAF measurements done on wide video datasets.
The emerging VVC standard was studied and was used within BD-PSNR and BD-VMAF measurements with x265, VP9, SVT-AV1 encoders. VVC gave the best results followed by SVT-AV1 and VP9.

Organization
We have organized the chapters in the following order: • Chapter 1: Video Streaming & CODEC Wars The first chapter provides motivation, a thesis statement and a description of the primary research contributions.
• This chapter also provides subjective video quality assessments for x265, VP9, SVT-AV1 Codecs for Spatio-Temporal datasets.
• Chapter 8 provides a conclusion and suggestions for future work. configurations. The current chapter recommends the use of a multi-objective optimization framework for selecting optimal encodings that can be subsequently used for solving constrained optimization problems that are functions of quality, bitrate, and encoding time. The proposed optimization framework is used to select optimal configurations from 3,600 possibilities based on GOP configurations, the quantization parameter, deblocking filtering, sample adaptive offset, and software presets that control the coding tree unit size (CTU size), prediction sizes, and the transform unit sizes.
We implement our approach using the x265 encoder and demonstrate on an example from the UT LIVE video quality database [1,19,20], and a second standard 2K video example from [3]. The results demonstrate the success of the proposed approach by selecting optimal configurations and eliminating sub-optimal encodings. In order to select an optimal configuration, we then define optimal communications modes as (also see related work in [25][26][27]). Here, the goal is to find optimal solutions subject to realistic constraints on encoding (T ≤ T max ), bitrate (BPS ≤ BPS max ), and image quality (Q ≥ Q min ). We are then primarily interested in optimal modes defined as [26]: (i) minimum encoding time mode, (ii) minimum bitrate mode, and (iii) maximum video quality mode, subject to opposing constraints from the two remaining objectives.
There are several challenges associated with the application of the multi-objective framework to HEVC encoding. First, we note that the Pareto-front will significantly vary from video to video, and even from GOP to GOP within each video. In [25,26], the authors considered a bottom up approach that allowed the variation of DCT hardware cores and the quantization parameter (QP) for each image. In [27], in another bottom-up approach, the authors considered a multi-objective optimization approach that was applied to HEVC intra-coding.
Here, we take a top down approach where we consider the development of a unifying approach for all HEVC modes. Second, it is important to acknowledge that the current x265 encoder for HEVC [21,28,29] provides a very sparse sampling of the space of encoding time -video quality -bitrate. Unfortunately, such sparsity imposes fundamental limits on the usefulness of the proposed, multi-objective optimization framework [30]. Thus, to address this problem, the current chapter introduces extended HEVC presets in x265 that include new GOP configurations.
This combination of new GOP configurations with the variation of QP, De-blocking filtering, and other parameters produces a large number of optimal configurations that allows for significantly better sampling of the multi-objective space. Third, the use of extended HEVC configurations requires the compression of each video under each one and can thus impose significant storage requirements. To address this issue, we introduce an offline approach that only stores the optimal configuration parameters (without the compressed videos) associated with the Pareto front. Then, the optimal configuration is selected by solving the optimization problem associated with each optimization mode. The optimally compressed video is then reproduced by running the x265 encoder with the optimal parameters.
In terms of related work, we also mention earlier research focused on the use of multiple objectives in hardware implementations, unrelated to video compression.
We have the use of parallel cores for single-pixel processors in [31], the development of one-dimensional filtering in [32], and two-dimensional filter bank approaches in [33].
The current chapter differs significantly from these previous hardware approaches applied to digital filtering by focusing on a top-down approach.
The rest of the chapter is organized into four sections. In section 2.2, we summarize the methodology. We provide the results in 2.3 and give concluding remarks in 2.4.

Methodology
We summarize the proposed method in Figure 2.1. For each given video, we present the computation of the Pareto front based on the GOP configurations, the HEVC profiles, and related parameters.
As stated earlier, the resulting Pareto front is simply expressed in terms of a mapping from each optimal GOP configuration, HEVC profile, and related parameters to the three objective functions (video quality, encoding time, and bitrate requirements). For any given optimization mode, we select and apply the optimal encoding configuration as shown in Fig. 2   In this mode, the goal is to minimize encoding time provided that the video can be communicated within the given bitrate and it is of sufficiently good quality.
• Minimum bitrate mode:  In this mode, the goal is to minimize bandwidth requirements provided that the video is of sufficient quality and we do not spend a large amount of time encoding it.
• Maximum video quality mode: Here, the goal is to reconstruct the video with the highest possible video quality that does not require more bandwidth that is available and within reasonable encoding time.

Conclusion
In this chapter, we have presented a unifying framework that allows us to jointly optimize for encoding time, bitrate, and image quality. We introduced new GOP configurations that allow for fine optimization control. The system has been demonstrated to work well with videos characterized by translational motions.

Adaptive Video Encoding based on
Camera activity Classification

Introduction
We present a framework for adaptive video encoding based on video content. The basic idea is to analyze the video to determine camera activity (tracking, stationary, or zooming) and then associate each activity with adaptive video quality constraints.
We demonstrate our approach on the UT LIVE video quality assessment database.
We show that effective camera activity detection and classification is possible based on the motion vectors and the number of prediction units used in the HEVC standard. In our results, by applying leave-one-out validation, we get a 79% correct classification rate. We also present two examples for real-time, high-quality video encoding achieving bitrate savings of 35% and 51.5%.
The current chapter considers an adaptive encoding framework for effective video communications. Our goal is to automatically detect different video activities and associate quality constraints based on a specific task. Thus, we effectively compress the video for specific tasks that can be adjusted by the users or the owners of the video content.
To begin with, we note that video quality assessment is an area of active research as discussed in [34][35][36][37]. In our case, we consider a simple and fast method for assessing image quality based on SSIM as discussed in [38]. Furthermore, our approach is motivated by the well-known fact that visual attention is task dependent as documented in early research reported in [39] and also more recently in [40]. We present an example that demonstrates camera tracking, zooming, and then held stationary in Fig. 3.1. Originally, the camera is following a presenter while he is pointing at different images of shields (see Fig. 3.1(a)). Once a particular shield of interest has been found, the camera motion ends, and the camera remains stationary on the target (see Fig. 3.1(b)). Then, the camera zooms on the target shield as shown in Fig. 3.1(c). For this example, we would require higher video quality during  zooming and when the camera is held stationary over the found target.
A second example that demonstrates different priorities is shown in Fig. 3.2.
Here, as shown in Fig. 3.2(a), the camera is tracking the man as he runs. Then, the camera stops tracking as the man stands still for the remaining of the video (see Fig. 3.2(b)). Clearly, if we are interested in identifying the region where the man stops, we would require higher quality during the stationary phase of the video.
The current research is an extension of earlier, related work on selecting optimal   HEVC encodings based on multi-objective optimization as reported in [41]. In [41], our focus was to select optimal video encoding for entire video sequences. The current paper represents a significant extension over [41] by developing an adaptive encoding paradigm.
The rest of the chapter is organized as follows. In section 3.2, we describe the underlying methodology and provide results in section 3.3. Concluding remarks are given in section 3.4.

Methodology
In order to implement the proposed adaptive video encoding approach, we will first need to develop a video activity classification system. Here, we develop a camera activity classification system based on HEVC features so as to minimize the computational complexity of our approach. We present a system diagram that summarizes the components of the adaptive video encoder in Fig. 3.3 We begin with a description of the camera activity classification system. Initially, we encode the video using B2 GOP since this basic prediction mode is subset to more advanced GOPs [41]. The bidirectional motion vectors (MV) and the number of prediction units (PU) are extracted from the encoded video to be used in the classification process. For feature vectors, we compute the magnitude and orientation histograms of the motion vectors using 25 bins and use them to provide estimates of the corresponding cumulative distribution functions (CDFs). We perform a nonparametric test to select histogram bins that can differentiate between the camera activities. Furthermore, using the selected features, we consider the use of a fast K nearest neighbor classification.
For dynamic adaptation, we rely on the use of the percentage change in the number of prediction units to detect camera activity changes. To understand how this  works, consider a change from a moving camera tracking the object to a stationary position. Due to the complexity of the camera motion, tracking would be expected to include a substantial number of prediction units. On the other hand, when the moving camera becomes stationary, the number of prediction units will be substantially reduced as the complexity of the motions is also substantially reduced. Similarly, there will be a substantial increase in the number of prediction units when going from stationary camera to zooming. Furthermore, note that a tracking (moving) camera will normally stop moving before zooming.
Once the camera activities have been successfully classified, we associate different video quality constraints for each task. For efficient video encoding, we consider the implementation of the minimum bitrate mode associated with the DRASTIC mode described in [41]. Here, we compute optimal QP and GOP encodings by solving: where EP denotes the encoding profile, BPS refers to the bits per sample, Q min refers to the minimum acceptable video quality, Q refers to the achieved video quality, T refers to the encoding time, and T max refers to the maximum allowable encoding time.
Thus, in (3.1), we can achieve real-time encodings by controlling T max and control encoding video quality by adjusting Q min . To solve (3.1), we can use the Pareto-front of optimal encodings as discussed in [41].

Results
We begin with a summary of camera activity classification. We then present results For camera activity classification, we establish ground truth by manually segmenting the UT LIVE video quality databases into tracking, stationary, and zooming if the tracking classification wins in the two corresponding binary classifiers, the activity is classified as tracking. We present the full confusion matrix in Table 3.3.
Classification results were computed using leave-one-out cross validation.
From the results, it is clear that we can correctly classify camera activity from the HEVC features. The impact of misclassification is minimized when we consider  We next present adaptive video encoding results for the shield and park run videos considered in the introduction. For all video segments, we maintain high video quality requirements by requiring that SSIM remains above 0.85 (see [34]). For all cases, we require real-time encoding performance using the x265 software [24]. The basic idea is to maintain high video quality requirements during video regions of interest and reduce the requirements over the remaining video regions. For both video examples, our approach selected the correct encoding modes associated with each assigned task. Refer to Table 3.1 for the full description of the adaptive constraints that were selected.
For the shield video example, we have a reduced video quality requirement over Chapter 3. Adaptive Video Encoding based on Camera activity Classification 29 the long tracking portion of the video as described in Table 3.1. On the other hand, we maintain high quality over the stationary and zooming portions of the video.
As a result, we have substantial bitrate savings of 35%. Bitrate requirements were reduced from 996.22 kbps to 640.86 kbps.
We also present results for the park run video in Fig. 3

Conclusion
This chapter presented an adaptive encoding method that uses video content to determine constraints on video quality for real-time encoding. The basic approach was demonstrated on identifying camera motions but could be extended to cover other types of video content. Overall, the approach shows that substantial bitrate savings can be attained depending on the length of the activity of interest. Netflix applies a brute-force approach of encoding each title/film category into 120 codec and bitrate combinations [44]. Each of these streaming platforms has its own encoding ladder, meaning that it targets specific bitrate per resolution such that the streams are encoded without significant artifacts. But this "One-size-fits-all" bitrate ladder, even though it achieves good quality encodings for certain bitrates, the methods cannot adapt to high camera motion or complex scenes. Given the diversity of Netflix movies/titles, this static encoding might store and encode video titles with best quality but not necessarily the optimal one because the static solution might store more bits than the allocated budget to achieve the same perceptual video quality.
Hence, we have the development of Per-Title encoding [45] which use machine learning techniques to select a couple of hundred encodings from a much larger set of possibilities. The selected bitrate-resolution combination tends to be efficient, in the sense that the encoded video is of high quality for the target bitrate. Netflix introduced the Video Multimethod Assessment Fusion (VMAF) [46] video quality metric to measure quality at different Constant Rate Factor (CRF) levels and bitrateresolution pairs. Netflix uses the VMAF scores to identify the best quality resolution at each applicable data rate. The method only works for a smaller video dataset and it requires extensive computing resources to run hundreds of encoding combinations for each title/movie. In contrast, Per-Chunk [47] pushed boundaries to deliver videos at low bitrates especially using VP9 [13] and H.264/AVC [48].   HEVC provides new encoding configurations that allow users to compress videos using different presets that offer internal trade-offs with the encoding tools and provide a variety of mode decisions in rate-control to effectively encode videos for a given target bitrate or constant quality. The default preset in x265 is set to medium performs with good quality encodes without considerably overclocking or overusing the CPU resources as this implementation of HEVC encoding standard is known for its highly parallelized, multi-threaded operations which enables fewer options in the rate control so that the encoding is processed at real-time in a more efficient way.
Compared that to the placebo mode, which is the last preset enables all the major encoding mode decisions for rate control and can produce the highest video quality but at the expense of enormous computational cycles and slower encoding times. So these presets each of them has a selected amount of mode decisions and as we go higher in terms of speed, x265 performs a faster encode but the compression efficiency is not at the best compression ratio which provides the means to define other presets and so on. Similar, to the encoding mode presets of x264 from Ultrafast to Placebo x265 has been implemented to provide a wide variety of encoding decisions to obtain the best bitrate compression ratio. These presets combined with different rate-distortion optimization (RDO), mode decisions, tuning parameters and with GOP structured can achieve the optimal quality without spending too much on the bits. As described in Section 2. In this chapter, we will develop methods to build forward regression models and inverse Newton's equations which will be adapted according to the DRASTIC operating modes. At the times of this thesis writing, the world is facing [11] COVID19 crisis where severe bandwidth limitation has occurred and all streaming platforms have reduced their bandwidths. DRASTIC provides very fine tuned solutions with higher video quality at low bandwidth scenarios and adapts with acceptable video quality well within the recommended bitrate ladders. The rest of this chapter is organized as follows. In section 4.2, we describe a brief account on video quality metrics, the underlying methodology and provide results in section 4.3 and concluding remarks Fair, 80-Good, and any score ≥ 90 -Excellent) as it has been trained using encoders ranging from CRF 22 1080p (highest quality) to CRF 28 240p (lowest quality). The former is mapped to score 100 and the latter is mapped to score 20. Also, in order to have a noticeable difference in visual quality, a VMAF score difference of at-least 6 Chapter 4. Segment-based x265 encoding with adaptive Local Pareto models 37 should be established. In video quality research conducted by Netflix, visual quality degrades primarily due to two types of artifacts: • Artifacts due to lossy Compression and • Artifacts due to scaling (low bitrates, rebuffering [54], Lower bandwidth scenarios ,rebuffering [55]). • Visual Information Fidelity (VIF) [59] A Full-reference image quality assessment metric that is built upon natural scene statistics of an image and correlates well with human visual system. VMAF uses a modified version inside the framework and governs the image quality of each video frame.
• Detail Loss Metric (DLM) [60] measures the loss of detailed information due to compression artifacts and textures of objects which severely impact the subjective quality. Both VIF and DLM represent the spatial feature representation of the video frames.
• Motion. The last feature is a temporal feature information of the video. This is achieved by calculating the temporal differences at pixel level of the luma component Y between adjacent frames. By calculating this temporal feature, we obtain the motion characteristics of the video.

Video Encoding Configurations
We present a top to bottom approach in describing the proposed method in  while (more video GOP segments to encode) do 6: Allocate Constraints and choose Optimization mode 7: C ompute all available configs Cfg1, Cfg2 8: and QP ranges QP i and QP n for different GOPs 9: Combine Configs and QP ranges into 10: candidate sets C all and QP all 11: Compute predicted objective values:

12:
PSNR all, VMAF all, FPS all, Bits all 13: by applying the Forward Regression models to 14: candidate sets C all and QP all.

15:
Compute Pareto-front by eliminating 16: Points whose objectives are not 17: Pareto-optimal 18: F ind the Optimal encoding Parameters 19: selecting the C Opt, QP Opt by Newton's method that 20: produce points that lie on the Pareto-front 21: candidate sets C all and QP all.

22:
Robust parameter estimation and optimization for next segment 23: Apply QP all and C all based on the current model.

24:
Solve optimization problem using local search.

25:
if either QP all or C all is out of range then 26: Update constraints and fix encodings 27: new estimates of QP and Cfg

30:
Enforce QP and Cfg within valid ranges. for current GOP segment S ave by applying the regression models to 38: candidate sets C all and QP all.   The need for an adaptive and dynamic video encoding implies that current systems use constant quality mode or constant rate factor as recommended by the encoding ladders [4], and there is no guarantee that these static systems provide efficient bitrate savings or render the video with a higher quality. All of these static systems employ one set of encoding parameters for all the videos not taking into consideration the varied motion content, textures and frame rates. The proposed dynamic system framework encodes videos by breaking them into small segments and then encodes them with different encoding combinations with various GOP structures.
Also, this method is applicable to videos with varying spatio-temporal characteristics and different camera motions that occur in the video. For effective usage of bits, we employ the QP and for overall image quality we utilize both the filters. More encoding parameters can be added but we wanted to demonstrate the effectiveness of a simple segment based encoding system that offers greater flexibility in choosing the encoder parameters. Though it is an exhaustive encoding system but only for the first segment and then the forward model adapts as the video progresses. The Pareto modeling follows the constraints and does an efficient job of predicting the encoding parameters for the next segment without re-encoding. We present a system diagram that summarizes the components of the Segment based Local Pareto Models with DRASTIC modes in Table 4.1.

Build Forward Regression Models based on x265 Configurations
We build the forward models with the encoding configurations as shown in the Table   Though it is an exhaustive encoding system but only for the first segment and then the forward model adapts as the video progresses. The pareto modeling follows the constraints and does an efficient job of predicting the encoding parameters for the next segment without re-encoding the next segment. Our proposed method build forward models from the encoding configurations as an off-line system to build the modelsfor the first 3 seconds and then deploy the models to predict the encoding parameters instead of plain encoding the whole video segments. The model predicts the encoding objectives, filter settings and the quantization parameter and gives to the encoder resulting in an optimal way of encoding.Initially, several linear regression methods were explored and studied carefully with statistical package Python [64] different models fitting the Pareto points.
The model building is a cumulative process since we have to exhaustively combine so many different encoding configurations and then obtain the resulting objectives along with its parameter setting and store them as tables. For each GOP structure encoded we obtain the pareto points which is used in the model building with various encoding combinations and the resulting optimal models are saved to be used for the next segment. For all of these model fittings, the order of the model equations are varied from linear, quadratic and cubic order and also this varies depending on the video content.While constructing a model, we considered many parameters for the equation like Open/Closed GOP structure, different presets, tuning settings but it only made the modeling complicated. Hence, we tries Step-wise regression to find which parameters had a significant impact on the response variables. We simplified the model equation which started from 6 predictor variables to 2 which are QP and SAO and DBSA filters. Qp, being an integer has significant impact on the quality of the video as it controls the step size of the quantizer inside any codec and directly affects the rate-control mechanism. Deblocking and SAO filters on the other enhance the frame quality during reconstruction inside the codec buffer. So both, these variables have significant effect on the quality of the video and also they are simple two variable equations. 3 represent QP coefficients and, α 0 , α 1 , α 2 , α 3 denote the constants of the polynomial regression equation.
We spent a lot of time on regression analysis to generate different model equa-

Results and Discussions
We begin with a summary of DRASTIC modes of operation for adaptive video encoding.
• Maximum video quality mode: Here, the goal is to reconstruct the video with the highest possible video quality than does not require more bandwidth that is available and within reasonable encoding time.
• Minimum bitrate mode: In this mode, the goal is to minimize bandwidth requirements provided that the video is of sufficient quality and we do not spend a large amount of time encoding it.
• Maximum encoding Rate mode: In this mode, the goal is to maximize the frame rate provided that the video can be communicated within the given bitrate and it is of sufficiently good quality.
All of the  Here we break the video into 3s segments which gives a total 4 segments with three 3 second segments and one 1s segment. We then encode each segment with the following settings to achieve what YouTube recommended as a bitrate for that resolution which is summarized in

Conclusion
We have proposed and demonstrated segment based adaptive video encoding systems using regression equations that provided significant bitrate savings with high video quality. The results also show DRASTIC's efficient adaptation at GOP level and provide much flexibility in terms of encoding rather than exhaustive computing or a sophisticated neural net. This segment-based encoding significantly has outperformed recommended bitrate approaches and provided better precision than recommendations by YouTube.

Chapter 5
Overview of Google VP9 Codec with Segment-based encoding at GOP level

Background of VP9 Video Coding Format
VP9 is the open source coding standard developed by Google [13], competitor to the H.265/HEVC [21] standard, and is considered to be the successor to VP8 [67,68] codec which is the equivalent to H.264/AVC [48]. VP9 codec was mainly adopted by YouTube which is playable on internet browsers, video players and also to stream its videos [4,43]. In contrast, HEVC was not adopted by none of the software and it's always a power-of-2 so that a frame can be divided into a maximum of 4 tiles depending on resolution. For example, a 480p video can have only 2 tile-columns or 2 tile-rows (or 2 tile-columns) and it can be processed independently by 4 thread enabling multi-threading. For 720p and 1080p, the number of tile-columns is 4 and so the total thread would be 8 respectively. As of now, VP9 does not support "slices".

Group of Pictures-GOPs
In VP9, there are 3 different types of frames: • Golden Frame (I Frame) -A key frame or an Intra frame which is inserted between scene changes.
• AltRef frame (Non-displayable) -Alternate reference frames which is not displayed in the bitstream but used in compound prediction and functionally very similar to B Frame.
• Last frame (Previously Encoded frame) -is the last fully decoded frame and it is visible in the bitstream.
Let us look at an overview of the GOP structure in VP9 standard. The GOP structure shown in Figure  reference hereafter referred as AltRef frames provide a round about to B-frame Prediction [21] or in VP9 called as Compound prediction where these frames are used in the references but are not displayed in the bitstream. The more alternate reference frames give the better prediction for a particular frame. Each AltRef is used as a reference point to a keyframe on a GOP interval which is defined as the minimum distance between two Intra frames or the Intra refresh interval.
As part of our study with VP9 codec, we introduced AltRef at different key intervals and came up with different GOP structures namely ALT0, ALT1, ALT2, ALT4, ALT6 based on the number of alternate reference frames. VP9 reference specification [13] states that we can have a maximum number of 6 frames. When there is no AltRef frame represented by ALT0, the GOP is entirely made up of only Golden and Last frames which is very similar to Zero Latency(IP) mode in x265 [24]. By default, VP9 used ALT1 as its GOP structure which provides one of the fastest encodes as similar to low-delay webRTC applications and when all of the AltRef frames are used for referencing we call it ALT6 which provides efficient encodes in terms of bitrate and compression ratio. Additionally, these AltRef can be constructed from other past AltRef frames or future frames in the bit stream in order to reduce the total bitrate overhead.

Intra Prediction
Intra prediction is less complex in VP9 as it has 10 intra directions, 8 angular, one DC and True Motion (TM) compared to HEVC which provides 35 directions. DC mode in Figure 5.3 is where we take the average of all pixels of the current block.
True motion refers to prediction mode where each pixel is subtracted from the topleft pixel array from its current block position.

Inter Prediction
Inter prediction [69] in VP9 is similar to other standards except it uses only 3 ref- of motion vectors are: • Nearest MV -Candidate neighborhood Motion vectors from current frame.
• Near MV -Candidate Motion vectors which is co-located MVs in the previous frame.
• Zero MV -Where there is no motion.
• New MV -To be transmitted in the frame with a motion vector reference (Nearest MV and Near MV).
In Figure 5.6, inter prediction blocks show the inter mode in blue, with the motion vectors in orange and the different types of MVs and the reference frames (L for Last, G for Golden and A for AltRef). The AltRef is chosen because of its availability from future frames in the reference buffer which greatly enhances the flexibility of the prediction.

Loop Filters
Loop filters in VP9 are very similar to HEVC standard with different filters for sharpening, blurring the reconstructed image, and for noise reduction in the alternate reference frames. Filter strength can be adjusted to vary from 0 to 6.

Segmentation
A new addition to VP9 encoder was segmentation.

Segment-based Encoding with VP9 Configurations
In this section, we will employ the proposed method in Figure 4.1 using VP9 codec as the encoding framework. The pseudo-code in Figure 4.4 is similar to the x265 segment based encoding except here we build it upon VP9 codec configurations.
In VP9 we start by splitting the video into 3 sec segments and encode them with different GOP, QP, filter combinations as a function of encoding configuration and for each objective video quality, bitrate, and encoding rate/time we obtain models to be used under their respective DRASTIC modes.
The number of VP9 encoding configurations is so large that deciding which configurations to use for encoding in VOD based applications was cumbersome. We used a familiar approach followed in x265 where we started off with different GOP structure and noticed whether they can be applied to VP9 encodings as well. We have summarized a whole list of VP9 encoding configurations which we used in the segment-based encoding in Table 5.1. Firstly, VP9 uses all different GOP structures and their CPU preset parameter which decides both the encode quality and the speed. The best setting for the CPU preset is similar to the placebo setting for X265. Thus, the best setting is extremely slow while providing excellent video quality. If the CPU preset is set to good, then the speed can be adjusted in the range of 0 to 5. At a set speed of 4 or 5, the encoder will turn off the Rate-Distortion Optimization(RDO) which will disregard the quality. We tried CPU preset set to good, with speed set to 3 and 2, but we found the encoder was still slow and was not utilizing all the cores. We then applied the CPU preset to rt which is quite faster and utilized the CPU cores and we changed the CPU preset to 4,8,12,16 and we found that the encode quality at CPU=8 and above settings gave fewer differences in the total bits and the storage size of each encoded video. So we decided to use the CPU preset to 8 with single pass encoding set as the quality as VP9 (build libVPx-Ver1.7) allows multiple rate-control methods and found this setting to be the best for VOD applications.  Regarding the GOPs, we found the encoding structures from the bit stream as described in Section 5.1 we want to use: ALT0, ALT1, ALT2, ALT4, ALT6 with different settings for the noise reduction for each alternate reference frame. The maximum number of references ARNR-maxframes for each AltRef was set to 7 with filter strength set to 5 and the ARNR-type to be 3. This ARNR setting was often used in VOD-specific frameworks [47,49,50,73] as the literature recommended that these settings do have an impact on the video quality. For the loop filters, we set the deblocking filter similarly to x265, with a setting of On and Off and we changed the QP range from 0 to 52 in steps of 4 since VP9 offers a maximum QP up to 63. All of these configurations were suited to row based multi-threading since VP9 uses parallel tiles so that our encodes run quite faster to evaluate this exhaustive list of combinations. VMAF was also incorporated into the encoding pipeline which calculates the VMAF score using the perceptual model [63] VMAF 0.6.1 and the corresponding VMAF scores are stored to be used for the Pareto models.

Parameter
With 200 encoding configurations, we fit the Pareto front using a small number of parameters. We measured Encoding Rate in the number of frames per second (FPS), Bitrate in kilobits per second and Video Quality using both PSNR & VMAF.
The local model predicts the objectives based on the constraints and, depending upon the DRASTIC [26] mode, can provide estimates for the next 150 video frames.
Different encoding combinations were considered before we finalized configurations that directly impact the encoding visually and the resulting compression ratio.

Forward Regression Models and Inverse Prediction in VP9
The model building process is quite similar to that in Section 4.1 except where we apply VP9 configurations to the model building process. Here are the model equations summarized, ranges from 0 to 1 and we found all models were following a similar trend as that of x265 with a small p-value of (≤ 0.05) [64] proving that the QP and Deblocking filter should be used for prediction.
Following the forward model for each GOP built, we satisfy the constraint optimization modes based on the selected DRASTIC operating mode to obtain the corresponding encoding configuration sets and constraints. For example, in minimum bitrate mode, we get constraints for quality and frame rates and then we apply them to the equations generated from the forward model with these as constraints.
Using Newton's [74] inverse equation, we find the optimal QP and Deblocking filter output from these equations taking into account video quality and encoding frame rate violations. For the minimum bitrate mode, we finally encode the video with optimal QP and Filters for the given segment make sure that the PSNR predicted is above the acceptable video quality and we want for the video to look better without much artifacts. This mode of operation simulates low bandwidth scenarios, where the video quality drops and degrades.
In simple terms, we basically take the forward model equation from encoding rate and bitrate and solve for a QP that maximizes the quality of the video. The QP value generated by the prediction is a floating point value and we approximate using an integer as the encoders accept only integer based QP values. Also, we note that the coefficients of the model fittings especially that of QP is more significant and have negative values which clearly state that increase in QP will decrease the bitrate and the video quality. Additionally, the forward model prediction might have induced some error due to fitting and will affect the prediction process during Newton's inverse method. So to compensate for the error, we allow soft violations say 10% for bitrates and encoding frame rates and 3-5% for video quality respectively.

Class C Basketball Drill 480p Video
We start with Class C 832x480p, 50fps from HEVC dataset. Basketball Drill video is 10s clip where a group of players practice in loop with the ball and keep running throughout the video. In this optimization mode the objective is to maximize the video quality while conforming to bandwidth constraints in terms of typical upload data rates as recommended by YouTube [4]. For 480p with 50fps the recommendation is 4000 kbps. We present the default mode in Table 5

Conclusion
The contributions of this chapter under VP9 codec we have proposed and demonstrated segment based adaptive video encoding systems using regression equations that have significant savings on bitrate provided with a high video quality. The results also show it is an efficient adaptation at GOP level and provides much flexibility in terms of encoding rather than exhaustive computing or a sophisticated neural net. This Segment based encoding significantly has outperformed standard recommended bitrate approaches and precision comapred to approaches that rely on stored pre-encoded on a large system. This chapter will focus on one of the implementations of AV1 standard via SVT-AV1 which stands for Scalable Video Technology-AV1 [16] Codec and is the first video codec co-developed by the Alliance for Open Media (AOM) [14]. SVT-AV1 is a joint collaboration between Intel and Netflix, members of AOM primarily built for video on demand, video transcoding, live streaming applications. Additionally, the codec is performance optimized targeting towards real-time encodings and higher performance supporting 1080p and 4K videos. We will provide an overview of the SVT-AV1 codec tools and then provide a summary of our proposed approach of segment-based encoding using this new SVT-AV1 codec.

Block partitioning
Originally, AV1 had a recursive block partitioning [76] system similar to VP9 and HEVC (64x64) with block sizes of 128x128 and all the way down to 4x,4 allowing each block to be further subdivided using 10-way partitioning for high-resolution videos. For example, the 128x128 block sizes can be split using quad-tree partitions into 10-way splitting starting from horizontal, vertical splitting and T-Splitting down to 4x4 blocks. Compared to previous video coding standards in VP9, we only had 64x64 blocks with recursive splitting down to 4x4 but with limited sub-block level 8x8 divisions at the 4-way partitions. AV1 extensively improves the partitioning and has more flexibility and control over 8x8 sub-blocks.

Group of Pictures -GOPs
In SVT-AV1, the number of reference frames are extended from 3 to 7 with the addition or naming the individual frames in the candidate reference pool as in libaom.
But the SVT-AV1 uses three-level Hierarchical B pictures in  In addition to the candidate pool, the frames are named as they are referenced for prediction: • Golden Frame (I Frame) -A key frame or an Intra frame which is inserted between scene changes.
• AltRef frame (Non-displayable) -Alternate reference frames which are not displayed in the bitstream but used in compound prediction and functionally very similar to b Frame in x265.
• AltRef2 frame (Non-displayable) -Alternate reference frames which are not displayed in the bitstream but used in compound prediction between the Golden and AltRef and functionally are very similar to b Frame in x265.
• BWD frame (Non-displayable) -Alternate reference frames used as an overlay between Altrefs and not displayed in the bitstream but used in compound prediction and functionally very similar to B Frame. pictures arranged temporally. In our proposed segment based encoding, we utilized this hierarchical system and, with different number of AltRefs, we were able to produce different encodings with coding efficiency and performance which will be discussed later in the next sections. The Intra Block copy utilizes those previously reconstructed blocks in the same frame by signaling an intra frame motion vector and effectively captures the content of the screen-shots with a lot of text.  The local warping model is estimated implicitly signal warping parameters for individual blocks. The motivation is to model real-time motions that cannot be simply represented by affine motion or cannot be estimated by homographic [78] motion models. Local warping models are estimated by using a linear curve fitting of neighborhood MVs and signal them into the bitstream which has an impact in the mode decisions as these parameters will be estimated after decoding. Combining both the local and global warp motion models is a great way of encoding but will be of higher computational complexity.

Transform Residual Coding
Transform partition sizes in AV1 range from 4x4 to 64x64 which is very similar to  that are yet to be implemented as the SVT-AV1 codec is still being finalized.
SVT-AV1 codec is complex and there are multiple configurations available to explore with different tuning options. SVT-AV1 codec is still in development and several tools from the AV1 standard are yet to be implemented. We decided to focus mainly on GOPs, filters and encoding presets for the use of segment-based coding as we did for x265. We have summarized a table of SVT-AV1 encoding configurations which we used in the segment-based encoding as shown in Table 6.1.
For SVT-AV1, figuring out the GOPs was the first priority. SVT-AV1 combines the libaom reference frames with that of the Random access GOP configuration which is predominantly used for video transmission and streaming. The problem with libaom is its naming conventions to B frames and P frames in order to circumvent the patent issues and henceforth the big confusion before we started the encoding process. We confirmed the GOP structure from the SVT-AV1 documentation [16] that they are using random access but with Hierarchical level B pictures. At the time of this study, only 3 and 4 layer hierarchical levels are employed while the latest versions of the SVT-AV1 comes with hierarchical levels 2, 3, 4, 5 respectively.
We made use of the AltRef frames along with these hierarchical levels and we found out they have different encoding rate, bitrate and PSNR on different test encodes. We found that there is a similarity with x265 which has B2 GOP, VP9 which has ALT2 and then in SVT-AV1 where we have hierarchical level 3 with being the fastest encode. We chose 7 for encode mode that will be suitable for single pass encoding system applicable in VOD [47,49,50,73] systems. The QP ranges here from 0 to 63 in SVT-AV1 similar to VP9. We set the lowest QP to 16 and all the way to 52 in steps of 4. The arnr-maxframes is set to 7, arnr-strength to 5 and the arnr-filter type set to 3 as per the default settings in the SVT-AV1 bitstream specifications. Since, the SVT-AV1 codec employs a multiple filtering system, we enabled them all ON/OFF like deblocking and loop restoration filter both to be ON/OFF. And again VMAF is incorporated into the encoding pipeline using the VMAF 0.6.1 perceptual model [63] since VMAF is not built into any of the encoders as part of the system. Also, the VMAF score obtained by using this VMAF 0.6.1 model might be different if we use VMAF 0.6.2/0.6.3 and so for consistency, we used the VMAF 0.6.1 model for our perceptual video quality score.

Forward Models and Inverse equation in SVT-AV1
We considered a total of 240 encodes per segment, and obtained the objectives PSNR in dB, VMAF, Bitrate in kbps, and Encoding rate in FPS. The model building process is quite similar to Figure 4.1 except here we apply these SVT-AV1 configurations.
Here are the model equations summarized, where β 1 , β i,1 , β i,2 represent QP coefficients and, α 0 , α 1 , α 2 denote the constants of the polynomial regression equation. For the SVT-AV1 model building, we assumed it will follow a similar trend as QP and deblocking filter is taken as the predicted variable with respect to the objectives PSNR, Bitrate, VMAF and FPS correspondingly. We fit the model and the objectives had a very good adjusted R square value (for PSNR, VMAF, Bitrate, FPS) with a score of 0.99, 0.99, 0.99 and 0.99, respectively. The p-value for all these models are (leq0.05) [64], proving once again that the QP and Deblocking filter both had a significant effect on the fitted model objectives.
Forward models are built for each GOP and we solve the constraint optimization modes based on the selected DRASTIC operating mode to obtain the corresponding encoding configuration sets and constraints. We allow soft violations say 10% for bitrates and encoding frame rates and 3-5% for video quality, respectively. We initialize the QP search with QP=30 and then use Newton's method to derive the optimal models. For example, in maximum video quality mode, we have constraints set for encoding rate and bitrates. Since we have multiple QP values generated after solving the inverse equation, we approximate the generated QP value to the nearest integer and apply it to the encoder to obtain the optimal objectives. For example, if the QP value predicted is 27.5, then in maximum video quality mode it will be rounded to QP=27.0 in order to obtain higher video quality. If it's a minimum bitrate mode, then we will round it to QP=28.0 that will minimize the bitrate without sacrificing quality. If the generated QP value does not obey the constraints, then a local search performed with the nearest values of QPs in the range of (QP+4 , QP−4) and then we try to obtain a newer QP, and then repeat the process again until we satisfy the constraints.

Class E Kristen and Sara Video HEVC 720p Dataset
We take Kristin and Sara video as shown in Figure ?? from Class E 1280x720 from HEVC dataset and from YouTube [4] recommendation the bitrate is 5000 kbps. For the default mode we found that QP=20 achieves a total bitrate of 5070.03 kbps.
Kristin and Sara video is a standard example of stationary video with a static back-ground and people interacting in a conversation without any complex motions. The default recommended settings and DRASTIC tables are summarized in Table 6.2 and 6.3 respectively. The GOP model equation tables are provided in Table 6.4 and 6.5 respectively.  Table   6.4 and 6.5. The overall gains for the maximum quality mode are provided in 6.6 and the corresponding chart in Figure 6.4

Chapter 6. SVT-AV1: A Scalable, Open Source AV1 Codec and Pareto Models105
Coefficients      Smoothing which enhances the angular directional prediction accuracy by employing 4-tap interpolation filters. Note that HEVC used only 2-tap interpolation for intra smoothing but since the intra angular directions are more wider, they need additional smoothing to improve the prediction accuracy. Another add-on to Intra coding is the Intra sub partitions wherein the intra block itself is predicted using one of the intra mode and the prediction error signal is transformed and quantized and reconstructed after inverse-transform and finally stored in the intra picture buffer. This reconstructed sub-partition is then used as a reference for the other sub-partitions for that block. It is quite a divisional way of prediction from sub-partitions within a partition. For this to work, all the sub-partitions have to be predicted by the same angular mode.

Inter Prediction -OBMC & Affine Motion
Inter coding in VVC is similar to HEVC except the motion compensation can now account for non-translational motion models which was never considered due to higher computational complexity. VVC features several new additions to the inter prediction tools mainly focusing on multiple motion merge modes. A prominent addition to VVC is the introduction of OBMC and affine motion.

Introduction
Several comparison studies [28], [86][87][88][89][90] of the encoders from different encoding standards have been done over the past years. These comparisons take into account the coding gains in terms of compression efficiency, performance over different bitrates.
In this subsection, we provide a background of various codecs compared and our approaches to objectively measure the performance of the emerging VVC encoder, AOM/SVT-AV1, HEVC/x265 and VP9 codecs for Video-on-Demand (VoD) streaming applications.
Our motivation for this section is derived from the IEEE Spectrum article in [91] where different codecs from encoding standards are compared [12] [3]. In this chapter, we will first explain the various codec configurations of each encoder and the encoding tests, and then we move to subjective video quality assessment.

Video Content Description
Video datasets that are used in the codec evaluation and their content description are provided in the following Tables I, II

Video Codec Configuration Setup
Each video codec chosen from a video coding standard and its encoding parameters are enlisted in Table 7.5. For x265, encoder we selected its default settings and Ultrafast preset enabled for Video On Demand (VOD) adaptive streaming applications.

BD-Rate Bjontegaard Metrics
The BD-metric or the Bjontegaard Delta measure is an effective method to compare the performance of two codecs over different rate-quality points. In order to measure the BD-rates, we compute a RD points at four different QP levels and then we fit a cubic polynomial to the points for each of the codec. All the bitrate values are converted into log values. Then, the overlap or the area between the two fitted curves is computed, which infers the rate of average bitrate change to an equivalent PSNR or vice-versa.

BD-PSNR
We take PSNR as our standard objective video quality measure since it is mostly used in video encodings as an objective benchmark metric even though it does not correspond well enough visually. Most encoders calculate the average PSNR using three different components from the original raw file which is in YUV format. PSNR Y refers to luma component or the brightness intensity as a human eye is more susceptible to notice change in brightness than colors. PSNR U & PSNR V refers to chrominance components. We do a weighted average of all of them to measure the overall objective PSNR also known as PSNR611 or Global PSNR using:

BD-VMAF
We take VMAF for our subjective video quality metric as it is currently the most popular metric that corresponds how the subject perceives the quality of the video.

BD-PSNR & BD-VMAF for UT LIVE Dataset
The dataset from UT LIVE VQA has a custom video resolution of 768x432 and VVC provides significant bitrate reductions against AV1 by 64.8%, x265 by 68.94% and 74.90% based on BD-VMAF, respectively. AV1 has lower BD-VMAF as shown in Table 7.9 with savings of around 7.30% against x265 and 12.80% against VP9.
The corresponding rate curves for VMAF is described in Figure 7.11. For BD-PSNR rates in Table 7 Tampere [3], respectively. The goal here was to study the subjective quality assessment with different resolutions and measure the VMAF perceptual metric simulating different video quality conditions. So we came up with three different scenarios.

Preparing Subjects to View and Assess the Videos
Three subjective experiment sessions were conducted separately on the test sequences in the three codec groups. All three experiment sessions were conducted in a bright lit room with 3H distance meaning the subjects [105] are seated from the screen at an optimal viewing distance, measured in inches. Subjects were briefed about the video quality assessment and were explained different artifacts in videos for example, at low bandwidth the videos might get blocky or pixellated and might be buffered.
We then ask the subjects on how would they evaluate the quality of video overall.
During the training session, few videos were shown with different video qualities and then the overall process was demonstrated. Then the subjects were given a scoring sheet and asked to score the quality in terms of 1-Bad, 2-Worse, 3-Fair, 4-Good,

5-Excellent.
All video test sequences were randomized and shown on a display, which is a SAMSUNG U28E690D LCD TV, with 4K screen resolution. There are 3 different groups: Group A(SVT-AV1), Group B(VP9) Group C(x265). Each group has been put through a training session where test sequences were shown and then explained on how to evaluate the video and then score them. A total of 32 subjects with an (a) Kimono with High Quality VMAF score 97.
average age of 27 (age range 21-55) participated for this subjective assessment.

Correlation Metrics Performance Comparison
We used two different correlation metrics:

Conclusion
An overview of the emerging VVC encoding tools was described and then BD-rates for both PSNR and VMAF for the codecs x265, VP9, SVT-AV1 against the reference encoder VVC-VTM were compared. Overall, VVC consistently beats the challenging competitor codecs and provides significant coding gains and performance. We also did a subjective video quality assessment with different video qualities encoded by varios codecs of the likes x265, VP9, SVT-AV1 and in this case the latter SVT-AV1 wins the majority both in terms of PSNR and VMAF by measuring their corresponding correlation metrics. VVC was not considered for this study as we do not have a conventional media player that can play .vvc bitstream files yet. Additionally, VVC is still in its early stages and with more tools need to be finalized and fully optimized so as for it to compete with libAOM or SVT-AV1 which is potentially the future in the video streaming industry.

Chapter 8
Conclusion and Future Work

Conclusion
We have provided a codec-agnostic dynamic framework that can be used to achieve better compression efficiency without sacrificing quality across different encoding standards. The segments based DRASTIC optimization approach is able to achieve coding gains compared against the YouTube recommended bitrates. We have targeted the constant QP method using single pass (1-Pass) to achieve optimal bitrate/resolution encodings, but this can be easily extended to 2-Pass, 3-Pass or other Multi-pass encoding methods.
The dissertation considered applications in H.265/HEVC, VP9, SVT-AV1, VVC Codecs by introducing new GOP structures for encoding and then applied DRAS-TIC optimization. The second application was analyzing different activities/camera motions in the video and then using Motion vectors on a frame level to classify the motion of the video content. Using this methodology, we then are able to adaptively encode videos and achieve 35% and 52% bitrate savings for the example videos.
Thirdly, we started to analyze the Pareto surface of each video and, after careful observation, we decided to do Segment-based encoding or breaking the video into 3-second segments and then build models by fitting the Pareto surface and by this we can simply capture the entire video content with few encoding parameters. Using these models, we can predict encoding parameters for the next segment using a fast approach that also satisfies dynamic constraints. We were able to conserve approximately 9% and 13% bitrate savings at 1080p videos using this approach. Currently, there are two pending publications that are derived from the dissertation. First, the DRASTIC Segment-based encoding with constrained video delivery for video compression standards x265, VP9, AV1 will be submitted as a full journal paper to the IEEE Open Access. Second, the comparison of the VP9, x265, SVT-AV1, VVC Codecs with VMAF as a leverage metric has been accepted to SPIE, Applications of Digital Image Processing XLIII, 2020.

Future Work
Segment-based encoding has been studied at the GOP level with selected encoding parameters for all the encoders. I recommend new set of directions for the future work which we looked upon but did not have the time and resources to complete it within the scope of this dissertation.
Simulated Annealing approach with 5% & 10% Encoding samples: We built the initial regression models using 250 encoding configurations for 3-second segments. It would be interesting to explore methods to reduce the number of required encodings.
For example, using a quite sophisticated sampling approach that uses simulated annealing with 5% or 10% samples from the original Pareto, we may be able to build a model and test it with different constraints for the videos.
Global Modeling approach with Scalable resolution: This modeling idea is to build the forward models for one resolution (e.g., 720p or 1080p), and then use the 1080p models for predicting the optimal encoding parameters for a different video segment.
To make it work, we would choose a dataset of the same resolution and segment the videos into 3s chunks and then build forward models on each of them.
In-order to effectively capture the entire video content, we have to use different encoding parameter sets as inputs to the encoding. Parameters like GOPs, QPs, Filters for reconstruction, Motion Vectors (MVs) and Motion Vector Prediction (MVP) at frame level, Residual Transform Unit (TUs) size and RDO decisions at the frame level. Using Leave-One-Out (LOOCV) Cross Validation, we can choose the optimal model for a particular video segment and then use it for a different video segment.
By scalable we mean the video can be scaled to different resolutions from the original resolution. For example, a 1080p model trained on a 3s video segment can be applied to predict the encoding parameters for a 720p video segment.
SVT-AV1 and libAOM Comparison for webRTC application: With ever growing VoD and streaming applications, we need more efficient and reliable systems that are ubiquitous and can deliver high quality video at extremely low bandwidth scenarios.
Since AV1 is the promised future codec for video delivery and streaming for real-time video communications, we can study it with the webRTC framework and deploy it in different networking conditions. DASH based Codec-agnostic Video Delivery system: With DASH based delivery gaining popularity, a client-server model with different encoders built in and switching based on video content on available constraints will be a very interesting study.