Experimenting texture similarity metric STSIM for intra prediction mode selection and block partitioning in HEVC
Karam Naser, Vincent Ricordel, Patrick Le Callet · 2014
Textures can often be found in large areas of images and videos. They have different spectral and statistical properties as compared with normal (structural) components. Encoding them with ordinary video coders requires higher bit rate and usually results are unsatisfying in perceived quality. Recently, different perceptual tools have been developed to estimate the perceived quality of textures taking into account models of human visual system. In this paper, we investigate and discuss the practical usability of one of these tools, namely STSIM, as a distortion function for selecting the intra-prediction mode and block partitioning of texture images in HEVC. We experiment few practical implementations to examine its performance compared with default metrics used by HEVC. Experimental results showed that the perceived quality of the decoded textures has been significantly improved specially for stochastic types of textures.