A Saliency Map Approach to Optimize VMAF for Video and Image Compression

The Video Multi-method Assessment Fusion (VMAF) has demonstrated a better correlation with Human Visual System than the conventional objective metrics, and has gradually gained adoption in the industry that needs to monitor the visual quality of compressed videos. However, due to its machine learning nature, it can not be expressed through a simple and explicit mathematical formula, which makes it difficult to incorporate VMAF into the rate-distortion optimization framework in video compression. In this work, we propose a new perspective that decomposes the VMAF as a superposition of spatial and temporal factors. The spatial factor, which also directly applies to image quality evaluation, is approximated by a saliency map. It in conjunction with the temporal factor approximated by the motion quantities allows a simple analytical formula that translates the mean squared distortion at each pixel to its impact to the overall VMAF metric. The proposed hypothesis is embedded into the rate-distortion optimization framework, and is experimentally shown to provide considerable coding gains in VMAF for both image and video compression.