A Hybrid Contrast and Texture Masking Model to Boost High Efficiency Video Coding Perceptual Rate-Distortion Performance

Abstract

first_pageDownload PDFsettingsOrder Article Reprints Open AccessArticle A Hybrid Contrast and Texture Masking Model to Boost High Efficiency Video Coding Perceptual Rate-Distortion Performance by Javier Ruiz Atencia 1,*ORCID,Otoniel López-Granado 1,*ORCID,Manuel Pérez Malumbres 1ORCID,Miguel Martínez-Rach 1ORCID,Damian Ruiz Coll 2ORCID,Gerardo Fernández Escribano 3ORCID andGlenn Van Wallendael 4ORCID 1 Department Computer Engineering, Miguel Hernández University, 03202 Elche, Spain 2 Department of Signal and Communications Theory, Rey Juan Carlos University, 28933 Madrid, Spain 3 School of Industrial Engineering, University of Castilla-La Mancha, 13001 Albacete, Spain 4 IDLab-MEDIA, Ghent University—IMEC, B-9052 Ghent, Belgium * Authors to whom correspondence should be addressed. Electronics 2024, 13(16), 3341; https://doi.org/10.3390/electronics13163341 Submission received: 27 May 2024 / Revised: 6 August 2024 / Accepted: 17 August 2024 / Published: 22 August 2024 (This article belongs to the Special Issue Recent Advances in Image/Video Compression and Coding) Downloadkeyboard_arrow_down Browse Figures Review Reports Versions Notes Abstract As most of the videos are destined for human perception, many techniques have been designed to improve video coding based on how the human visual system perceives video quality. In this paper, we propose the use of two perceptual coding techniques, namely contrast masking and texture masking, jointly operating under the High Efficiency Video Coding (HEVC) standard. These techniques aim to improve the subjective quality of the reconstructed video at the same bit rate. For contrast masking, we propose the use of a dedicated weighting matrix for each block size (from 4×4 up to 32×32 ), unlike the HEVC standard, which only defines an 8×8 weighting matrix which it is upscaled to build the 16×16 and 32×32 weighting matrices (a 4×4 weighting matrix is not supported). Our approach achieves average Bjøntegaard Delta-Rate (BD-rate) gains of between 2.5% and 4.48% , depending on the perceptual metric and coding mode used. On the other hand, we propose a novel texture masking scheme based on the classification of each coding unit to provide an over-quantization depending on the coding unit texture level. Thus, for each coding unit, its mean directional variance features are computed to feed a support vector machine model that properly predicts the texture type (plane, edge, or texture). According to this classification, the block’s energy, the type of coding unit, and its size, an over-quantization value is computed as a QP offset (DQP) to be applied to this coding unit. By applying both techniques in the HEVC reference software, an overall average of 5.79% BD-rate gain is achieved proving their complementarity.