SSIM-based distortion metric for film grain noise in HEVC

Abstract

In video coding, the sum of squared differences (SSD) is traditionally used for rate-distortion optimization (RDO). However, SSD has been known that has low correlation on subjective quality. In particular, film grain noise (FGN)-synthesized video sequence is a very good example of subjective quality degradation with SSD-based RDO. Therefore, structural similarity (SSIM) has been considered for RDO owing to its simplicity and high correlation with subjective quality. The SSIM metric was not designed to be used for previous RDO framework; additional processing, such as content analysis or adaptive Lagrangian multipliers, was required in previous studies. Based on analyzing cases of degradation in SSIM-based coding, this study proposes a novel SSIM-like distortion measure. In this paper, two objectives are considered. First one is FGN-synthesized video coding using the SSIM-like distortion measure to preserve noise pattern. Seconds, the proposed metric is designed for direct application in previously developed RDO frameworks without scene-analysis-based RDO. The experimental results demonstrate that the proposed method reduces erroneous prediction blocks and the Bjontegaard delta rate by 67.46% on average compared to original SSIM-based RDO for FGN-synthesized video sequences. The results show the proposed metric is effective for film grain noise in similar bit rate, compared to a high-efficiency video coding test model (HM16.6) and the original SSIM metric.