Experimenting Texture Similarity Metric STSIM for Intra Prediction Mode Selection and Block Partitiong in HEVC

Karam Naser (1, 2) , Vincent Ricordel (1, 2) , Patrick Le Callet (1, 2)

1 irccyn-ivc
2 IRCCyN-IVC

Textures can often be found in large areas of images and videos. They have different spectral and statistical properties as compared to 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 taken 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 parti-tioning of texture images in HEVC. We experiment few practical implementations to examine its performance compared to 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

Mots clés: Texture Coding; Perceptual Optimization; STSIM

Domaines: Traitement du signal et de l'image [eess.SP]; Traitement des images [eess.IV]; Traitement du signal et de l'image [eess.SP]

Karam Naser, Vincent Ricordel, Patrick Le Callet. Experimenting Texture Similarity Metric STSIM for Intra Prediction Mode Selection and Block Partitiong in HEVC. Digital Signal Processing (DSP), Aug 2014, Hong Kong, China. ⟨hal-01101084⟩