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Proceedings Paper

Verification testing of the compression performance of the HEVC screen content coding extensions
Author(s): Gary J. Sullivan; Vittorio A. Baroncini; Haoping Yu; Rajan L. Joshi; Shan Liu; Xiaoyu Xiu; Jizheng Xu
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Paper Abstract

This paper reports on verification testing of the coding performance of the screen content coding (SCC) extensions of the High Efficiency Video Coding (HEVC) standard (Rec. ITU-T H.265 | ISO/IEC 23008-2 MPEG-H Part 2). The coding performance of HEVC screen content model (SCM) reference software is compared with that of the HEVC test model (HM) without the SCC extensions, as well as with the Advanced Video Coding (AVC) joint model (JM) reference software, for both lossy and mathematically lossless compression using All-Intra (AI), Random Access (RA), and Lowdelay B (LB) encoding structures and using similar encoding techniques. Video test sequences in 1920×1080 RGB 4:4:4, YCbCr 4:4:4, and YCbCr 4:2:0 colour sampling formats with 8 bits per sample are tested in two categories: “text and graphics with motion” (TGM) and “mixed” content. For lossless coding, the encodings are evaluated in terms of relative bit-rate savings. For lossy compression, subjective testing was conducted at 4 quality levels for each coding case, and the test results are presented through mean opinion score (MOS) curves. The relative coding performance is also evaluated in terms of Bjøntegaard-delta (BD) bit-rate savings for equal PSNR quality. The perceptual tests and objective metric measurements show a very substantial benefit in coding efficiency for the SCC extensions, and provided consistent results with a high degree of confidence. For TGM video, the estimated bit-rate savings ranged from 60–90% relative to the JM and 40–80% relative to the HM, depending on the AI/RA/LB configuration category and colour sampling format.

Paper Details

Date Published: 19 September 2017
PDF: 11 pages
Proc. SPIE 10396, Applications of Digital Image Processing XL, 103960B (19 September 2017); doi: 10.1117/12.2276388
Show Author Affiliations
Gary J. Sullivan, Microsoft AI & Research (United States)
Vittorio A. Baroncini, GBTech (Italy)
Haoping Yu, Futurewei Technologies (United States)
Rajan L. Joshi, Qualcomm Inc. (United States)
Shan Liu, Futurewei Technologies (United States)
Xiaoyu Xiu, InterDigital (United States)
Jizheng Xu, Microsoft AI & Research (United States)


Published in SPIE Proceedings Vol. 10396:
Applications of Digital Image Processing XL
Andrew G. Tescher, Editor(s)

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