Dr. Qin Liu Profile

Dr. Qin Liu

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Proceedings Article | 10 November 2020 Paper

Backwards pre-analysis and flexible quantization group size adjustment based quality optimization for screen content coding

Ziyan Zhao, Yihang Li, Qin Liu, Songlin Du, Takeshi Ikenaga

Proceedings Volume 11584, 115840M (2020) https://doi.org/10.1117/12.2580429

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Quality optimization greatly improves coding efficiency by optimizing Quantization Parameter (QP). Recently, preanalysis module to evaluate temporal information propagation inter-frames is used to adaptively adjust QP for each coding unit (CU). However, considering characteristics of Screen Content Coding (SCC), problems exist in module design and QP generation part for screen content. For module design, SCC mainly uses intra-frame compression tools, which haven’t been included in current information propagation model. For QP generation, CUs in fixed-size quantization group (QG) are encoded with same QP, which is imprecise for screen content. This paper proposes intra-frame backwards pre-analysis method and flexible QG size adjustment method for SCC quality optimization. Proposed intra-frame pre-analysis method extends information propagation model by adding intra-frame analysis. Before compression, pre-analysis module finds all intra-frame references and generates importance for each micro block. Proposed flexible QG size is applied during compression and make QG size fit actual CU size. Thus, one QG only contains one CU, so QP of current CU is generated only from the average importance of micro blocks belonging to it, without influenced by micro blocks in other CUs. The experiment shows that proposed algorithm achieves 0.571% BD-rate reduction for the All Intra (AI) configuration with less than 5% encoding time increasing under the SCC common test condition. And the proposed algorithms are conducted in VTM version 5, the standard test environment for Versatile Video Coding (VVC). This work explores the potentiality of quality optimization for screen content and will make contribution to the development of next generation SCC standard.

Proceedings Article | 10 November 2020 Paper

Bottom-up check and temporal rate proportion based fast InterIMV algorithm in versatile video coding

Yihang Li, Ziyan Zhao, Qin Liu, Songlin Du, Takeshi Ikenaga

Proceedings Volume 11584, 115840W (2020) https://doi.org/10.1117/12.2581331

KEYWORDS: Copper, Video coding, Computer programming, Video compression, Distortion, Quantization, Video processing, Video, Statistical analysis

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The latest video compression standard Versatile Video Coding (VVC/H.266) adopts many advanced encoding tools which achieve larger BD-rate reduction but bring much time consumption. Among all new tools, an inter-prediction mode called InterIMV mode is most time consuming for occupying 12.7% of entire encoding time, with 96.8% of its check redundant in depth level and coding unit (CU) level. Current depth level mode skip methods do early termination based on original top-down structure and skip mode check in lower depth, which brings large quality decrease. For CU level, the common features are not effective to correctly skip InterIMV check. Thus, fast InterIMV algorithm targeted to remove InterIMV mode redundancy is needed. This paper proposes bottom-up InterIMV check method on depth level and temporal rate-distortion relation based check skip method on CU level. Firstly, proposed method executes InterIMV mode in a bottom-up order after all depths are traversed. This method checks InterIMV mode from the lowest depth towards upper depths and finds the suitable depth for current area. Then, InterIMV check in depths above suitable depth is removed without bringing much quality damage, since only the suitable depth is used to encode current area. Secondly, for each CU, the proportion of rate in rate-distortion cost (RD-cost) of temporal best mode is used to judge whether to skip InterIMV check, since this relationship is strongly associated with InterIMV selection. While testing on VVC benchmark VTM5 under RandomAccess configuration, the proposed methods achieve 79.78% speed up in InterIMV process on average. The BD-rate increase of proposed methods is only 0.37% which brings no significant coding efficiency decrease.

Proceedings Article | 12 June 2020 Paper

Multi-scale contextual attention based HDR reconstruction of dynamic scenes

Yipeng Deng, Qin Liu, Takeshi Ikenaga

Proceedings Volume 11519, 115191F (2020) https://doi.org/10.1117/12.2572977

KEYWORDS: High dynamic range imaging, Image fusion, Image restoration, Image quality, Convolution, Cameras, Reconstruction algorithms

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High dynamic range (HDR) image reconstruction of dynamic scenes from several low dynamic range (LDR) images captured with different exposure time is a challenging problem. Although several methods based on optical flow or patch-match have been proposed to address this problem, they are not robust enough and results still suffer from ghost-like artifacts for challenging scenes where large foreground motions exist. To this end, this paper proposes a multi-scale contextual attention guided alignment network called CAHDRNet and presents some evaluation results. In stark contrast to methods based on optical flow, this paper demonstrates that HDR reconstruction can be formulated as an image inpainting task and CAHDRNet conducts patch replacement on the deep feature maps unlike previous patch-based reconstruction methods. The contextual attention module proposed by an image inpainting work is extended in CAHDRNet with multi-scale attention rebalance to help model flexibly handle different scenes and reduce patch replacement error. Experiments on the public dataset indicate that the proposed CAHDRNet produces ghost-free results where detail in ill-exposed areas is well recovered. The proposed method scores 40.97 on test sequences with PSNR metric averagely while the best PSNR score of non-flow-based methods is 38.60. The flow-based method scores 40.95 with PSNR metric while it has 5 points better score than our result with HDR-VDP-2 metric. According to quantitative and qualitative evaluations, the proposed method outperforms all non-flow-based methods and has its merits and demerits compared with the flow-based method.

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