Perceptually Motivated Adaptive Quantization Algorithm for Region-of-Interest Coding in H.264

摘要

Encoding a video sequence at very low bit-rate with good quality remains a major challenge in video coding research. Improvements on Region of Interest (ROI) can provide better perceptual quality. This paper proposes an adaptive perceptual quantization algorithm for ROI video coding. Our goal is to find a solution to achieve significant ROI quality improvement and a perceptually pleasing background. Based on the masking effects of Human Visual System (HVS), the area inside ROI is divided into partitions by a grid model. And Macroblocks (MBs) are classified as non-ROI, grids inside ROI, and gradient region. The quantization parameters for different regions are modulated by an adaptive perceptual priority map. Extensive experiment results show that the proposed algorithm could improve the ROI objective quality by up to 4.14db compared by uniform quantization while with little loss of background. Subjective results show that the perceptual video quality is improved obviously.