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A unified active and semi-supervised learning framework for image compression

We consider the problem of lossy image compression from machine learning perspective. Typical image compression algorithms first transform the image from its spatial domain representation to frequency domain representation using some transform technique, such as discrete cosine transform and discrete wavelet transform, and then code the transformed values. Recently, instead of performing a frequency transformation, machine learning based approach has been proposed which uses the color information from a few representative pixels to learn a model which predicts color on the rest of the pixels. Selecting the most representative pixels is essentially an active learning problem, while colorization is a semi-supervised learning problem. In this paper, we propose a novel active learning algorithm, called graph regularized experimental design (GRED), which shares the same principle of the semi-supervised learning algorithm used for colorization. This way, active and semi-supervised learning is unified into a single framework for pixel selection and colorization. Our experimental results suggest that the proposed approach achieves higher compression ratio and image quality, while the compression time is significantly reduced.