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A multi-purpose objective quality metric for image watermarking

Knowing that the watermarking community use simple statistical quality metrics in order to evaluate the watermarked image quality, the authors have recently proposed a simplified objective quality metric (OQM), called “CPA”, for watermarking applications. The metric used the contrast sensitivity function, along with an adapted error pooling, and proved to perform better than state-of-the-art OQMs. In this work, we intend to improve the performance of the CPA metric. The new metric includes the most important steps of Human Visual System (HVS) based quality metric, namely spatial frequency consideration and masking effects. Besides, this work goes further than classical image quality assessment, and several objective quality metrics will be tested in a watermarking algorithm comparison scenario. We will show that the proposed metric is both able to accurately predict the observers score in a quality assessment task, and is also able to compare watermarking algorithms altogether on a perceptual quality viewpoint.