Luma Mapping with Chroma Scaling in Versatile Video Coding

Lu, Taoran; Pu, Fangjun; Yin, Peng; McCarthy, Seán; Husak, Walt; Chen, Tao; François, Édouard; Chevance, C.; Hiron, Franck; Chen, Jie; Liao, Ru-Ling; Ye, Yan; Luo, Jiancong

doi:10.1109/dcc47342.2020.00027

Cited by 25 publications

(6 citation statements)

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“…These contributions include: 1. signalling LMCS model parameters in the APS instead of the SH [58] to manage the temporal dependencies; 2. implicitly deriving chroma residue scaling factors from previously reconstructed neighboring luma samples to solve luma-chroma dependent issue [59]; and 3. optionally signalling CS offsets to fine tune the balance between luma and chroma coding performance [60]. The final LMCS design in VVC [62] combines all these contributions.…”

Section: A Development Of Lmcsmentioning

confidence: 99%

“…For SDR/HDR HLG, the algorithm is designed to optimize for PSNR. The basic idea of the encoder algorithm for SDR and HDR HLG videos is to assign more luma codewords to spatially smooth areas than non-smooth areas [61], [62]. The mapping curve is estimated only at IRAP pictures and signalled in the APS associated with the IRAP picture.…”

Section: Lmcs Encoder Parameters Estimationmentioning

confidence: 99%

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VVC In-Loop Filters

Karczewicz

Taquet

et al. 2021

IEEE Trans. Circuits Syst. Video Technol.

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This paper presents an overview of the technologies for in-loop processing and filtering in the Versatile Video Coding (VVC) standard. These processes comprise luma mapping with chroma scaling, deblocking filter, sample adaptive offset, adaptive loop filter and cross-component adaptive loop filter. They are qualified as "in-loop" because they are applied inside the encoding and decoding loops, before storing the pictures in the decoded picture buffer. The filters are complementary and address different purposes. Luma mapping with chroma scaling aims at adaptively modifying the coded samples distribution for improved coding efficiency. The deblocking filter aims at reducing blocking discontinuities. Sample adaptive offset mostly aims at reducing artifacts resulting from the quantization of transform coefficients. Adaptive loop filter and cross-component adaptive loop filter are adaptive filters enabling to enhance the reconstructed signal, using for instance Wiener-filter encoding approaches. The paper provides an overview of the in-loop filtering process and a detailed description of the filtering algorithms. Objective compression efficiency results are provided for each filter, with indication of cumulative coding gains. Subjective benefits are illustrated. Implementation issues considered during the design of the VVC in-loop filters are also discussed.

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Section: A Development Of Lmcsmentioning

confidence: 99%

Section: Lmcs Encoder Parameters Estimationmentioning

confidence: 99%

VVC In-Loop Filters

Karczewicz

Taquet

et al. 2021

IEEE Trans. Circuits Syst. Video Technol.

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“…The proposed method aims to improve the end-to-end coding efficiency for depth maps with the limited dynamic range by better utilizing the range and is based on the methodology of Luma Mapping with Chroma Scaling (LMCS) in VVC. LMCS allocates more codewords to a luma range where subjective distortion can easily occur in an input video [27]. The proposed method affords a good trade-off between bit saving and synthesized view quality depending on the depth map reliability in the 6DoF video compression.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear Depth Quantization Using Piecewise Linear Scaling for Immersive Video Coding

Park

Lim

et al. 2022

IEEE Access

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Moving Picture Experts Group (MPEG) is developing a standard for immersive video coding called MPEG Immersive Video (MIV) and is releasing a reference software called Test Model for Immersive Video (TMIV) in the standardization process. The TMIV efficiently compresses an immersive video comprising a set of texture and depth views acquired using multiple cameras within a limited 3D viewing space. Moreover, it affords a rendered view of an arbitrary view position and orientation with six degrees of freedom (6DoF). However, the existing depth quantization applied to depth atlas in TMIV is insufficient since the reconstructed depth is crucial for achieving the required quality of a rendered viewport. To address this issue, we propose a nonlinear depth quantization method that allocates more codewords to a depth subrange with a higher occurrence of depth values located at edge regions, which are important in terms of the rendered view quality. We implement the proposed nonlinear quantization based on piecewise linear scaling considering the computational complexity and bitstream overhead. The experimental results show that the proposed method yields PSNR-based Bjøntegaard delta rate gains of 5.2% and 4.9% in the end-toend performance for High-and Low-bitrate (BR) ranges, respectively. Moreover, subjective quality improvement is mainly observed at the object boundaries of the rendered viewport. The proposed nonlinear quantization method has been adopted into the TMIV as a candidate standard technology for the next MIV edition.

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“…VVC brings several novel techniques and enhancements for block partitioning, intra-and inter-frame prediction, transform, quantization, entropy, and in-loop filters to improve the encoding efficiency. These improvements include larger block sizes, flexible block partitioning using a Quadtree with nested Multi-type Tree (QTMT) structure [6], a higher number of angular intra-frame prediction modes [7], Multiple Reference Line (MRL) intra-frame prediction [8], Affine Motion Compensation (AMC) [9], Multiple Transform Selection (MTS) [10], Luma Mapping with Chroma Scaling (LMCS) [11], extended maximum Quantization Parameter (QP), improved implementations of quantization and entropy coding, among others [7].…”

Section: Introductionmentioning

confidence: 99%