Fast Inter-Mode Decision Algorithm for High-Efficiency Video Coding Based on Textural Features

Abstract

Due to the problems of the new generation of video coding standard HEVC (High-Efficiency Video Coding), such as high computational complexity and the large computation, This paper proposes a fast inter-mode decision algorithm based on image texture features and by using Sobel operator the edge features are extracted from CU which is partitioned by simulation, and then the final partitioning size of CU is determined by the texture features contained in the current CU block of simulation partitioning. By using this method, both of the traverse layers of CU depth and the times of inter predictive coding are reduced. Thus the computational complexity of coding terminal is lowered effectively. The experimental results showed that, compared with inter-mode decision algorithm in HEVC standard, the time of this method is saved 45.35% on average with little loss of coding efficiency and PSNR (peak signal-noise ratio). Index Terms-HEVC, inter frame, texture feature, coding depth I. INTRODUCTION The new generation of the video compression standard HEVC (High Efficiency Video Coding) was JCT-VC (Joint Collaborative Team on Video Coding) jointly established by ISO/IEC (MPEG) and Video Coding Experts Group (VCEG) of ITU-T, and it was determined in the first JCT-VT meeting convened in April, 2010 in Germany along with the test software HM(HEVC Model) [1]. HEVC was formally determined as the international standard in the 12th JCT-VT meeting convened in January of 2013 in Switzerland. It mainly serves highdefinition video which is gradually promoted now [2]. Compared with the H.264/AVC, the aspects improved by HEVC mainly are: (1) the size of macroblock extends from 16×16 of H.264/AVC to 64×64 so as to the compress for video of high resolution, at the same time, the luminance component has 35 defined prediction modes and the chrominance component has 6 in intra prediction technology; (2) flexible coding structure includes CU (Coding Unit), PU (Prediction Unit) and TU (Transform unit), and the CU of largest size is called as

HEVC optimizations for medical environments

“…Another approach to tackle this problem is to detect static regions in the image, which possess a similar movement, and homogenous blocks, which have a similar texture. Then it is unlikely to split them into smaller blocks [5][6][7][21][22][23][24], diminishing the number of required algorithm iterations. Hence, in order to keep the HEVC encoding flow as fast as possible, our work will exploit the homogeneous texture regions.…”

Inter Prediction Complexity Reduction for HEVC based on Residuals Characteristics

“…The AWMS collect feedbacks at a frame level and dynamically configures different parameters for the video-coding system such as the maximum CU depth and the search range. A fast CU size decision based on Sobel operator is proposed in [14]. By using textual features of the video images, the algorithm identifies the coding depth of the final CU without having to traverse the coding layers and results in a reduced computational complexity.…”