Sub-Sampled Cross-Component Prediction for Chroma Component Coding

Li, Junru; Wang, Meng; Zhang, Li; Zhang, Kai; Wang, Shiqi; Ma, Siwei; Gao, Wen

doi:10.1109/dcc47342.2020.00028

Cited by 19 publications

(9 citation statements)

References 20 publications

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“…In the initially adopted version of the CCLM mode, the linear minimum mean square error (LMMSE) estimator was used for derivation of the parameters [45]. In the final design, however, only four samples are involved to reduce the computational complexity [46], [47]. Fig.…”

Section: H Cross Component Linear Modelmentioning

confidence: 99%

Intra Prediction and Mode Coding in VVC

Pfaff

Filippov²,

Liu

et al. 2021

IEEE Trans. Circuits Syst. Video Technol.

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This paper presents the intra prediction and mode coding of the Versatile Video Coding (VVC) standard. This standard was collaboratively developed by the Joint Video Experts Team (JVET). It follows the traditional architecture of a hybrid block-based codec that was also the basis of previous standards. Almost all intra prediction features of VVC either contain substantial modifications in comparison with its predecessor H.265/HEVC or were newly added. The key aspects of these tools are the following: 65 angular intra prediction modes with block shape-adaptive directions and 4-tap interpolation filters are supported as well as the DC and Planar mode, Position Dependent Prediction Combination is applied for most of these modes, Multiple Reference Line Prediction can be used, an intra block can be further subdivided by the Intra Subpartition mode, Matrix-based Intra Prediction is supported, and the chroma prediction signal can be generated by the Cross Component Linear Model method. Finally, the intra prediction mode in VVC is coded separately for luma and chroma. Here, a Most Probable Mode list containing six modes is applied for luma. The individual compression performance of tools is reported in this paper. For the full VVC intra codec, a bitrate saving of 25% on average is reported over H.265/HEVC using an objective metric. Significant subjective benefits are illustrated with specific examples.

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Section: H Cross Component Linear Modelmentioning

confidence: 99%

Intra Prediction and Mode Coding in VVC

Pfaff

Filippov²,

Liu

et al. 2021

IEEE Trans. Circuits Syst. Video Technol.

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show abstract

“…The values of four downsampled neighboring luma samples are compared to determine the two minimum luma sample values, R L_D_MIN0 and R L_D_MIN1 , and two maximum luma sample values, R L_D_MAX0 and R L_D_MAX1 . Furthermore, two minimum chroma sample values, R C_MIN0 and R C_MIN1 , and two maximum chroma sample values, R C_MAX0 and R C_MAX1 , are derived from the corresponding four neighboring chroma samples [25,26]. For the equation of the straight line, two points, which represent the luma sample values in the x-axis and the chroma sample values in the y-axis, are obtained as follows: The luma sample values of two points, denoted by X P0 and X P1 , are derived by averaging the two minimum luma sample values, and averaging the two maximum luma sample values, as in Equations 3and 4:…”

Section: Description Of the Cclm In Vtm-60mentioning

confidence: 99%

“…Here, for example, the neighboring sample is considered as available when the neighboring sample exists in the same picture, slice, or tile where the current block belongs. As proposed in[25,26], from the available top and top-right neighboring chroma samples, R C (0, −1) to R C (W C_A − 1, −1), and the available left and left-below neighboring chroma samples, R C (−1, 0) to R C (−1, H C_A − 1), up to four neighboring chroma samples are selected by subsampling the neighboring sample positions as…”

mentioning

confidence: 99%

Simplification on Cross-Component Linear Model in Versatile Video Coding

2020

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To improve coding efficiency by exploiting the local inter-component redundancy between the luma and chroma components, the cross-component linear model (CCLM) is included in the versatile video coding (VVC) standard. In the CCLM mode, linear model parameters are derived from the neighboring luma and chroma samples of the current block. Furthermore, chroma samples are predicted by the reconstructed samples in the collocated luma block with the derived parameters. However, as the CCLM design in the VVC test model (VTM)-6.0 has many conditional branches in its processes to use only available neighboring samples, the CCLM implementation in parallel processing is limited. To address this implementation issue, this paper proposes including the neighboring sample generation as the first process of the CCLM, so as to simplify the succeeding CCLM processes. As unavailable neighboring samples are replaced with the adjacent available samples by the proposed CCLM, the neighboring sample availability checks can be removed. This results in simplified downsampling filter shapes for the luma sample. Therefore, the proposed CCLM can be efficiently implemented by employing parallel processing in both hardware and software implementations, owing to the removal of the neighboring sample availability checks and the simplification of the luma downsampling filters. The experimental results demonstrate that the proposed CCLM reduces the decoding runtime complexity of the CCLM mode, with negligible impact on the Bjøntegaard delta (BD)-rate.

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“…One is to optimize the selection of the direct mode (DM) used in chromaticity prediction candidate modes list [2]- [6]. The other is to optimize the prediction model used in CCLM to improve prediction efficiency [7]- [9].…”

Section: Introductionmentioning

confidence: 99%

A Fast Chroma Intra-Prediction Mode Decision Algorithm Based on Texture Characteristics for VVC

Liu¹,

Su²,

Yang³

et al. 2021

IEICE Trans. Inf. & Syst.

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Cross-component linear model (CCLM) chromaticity prediction is a new technique introduced in Versatile Video Coding (VVC), which utilizes the reconstructed luminance component to predict the chromaticity parts, and can improve the coding performance. However, it increases the coding complexity. In this paper, how to accelerate the chroma intra-prediction process is studied based on texture characteristics. Firstly, two observations have been found through experimental statistics for the process. One is that the choice of the chroma intra-prediction candidate modes is closely related to the texture complexity of the coding unit (CU), and the other is that whether the direct mode (DM) is selected is closely related to the texture similarity between current chromaticity CU and the corresponding luminance CU. Secondly, a fast chroma intra-prediction mode decision algorithm is proposed based on these observations. A modified metric named sum modulus difference (SMD) is introduced to measure the texture complexity of CU and guide the filtering of the irrelevant candidate modes. Meanwhile, the structural similarity index measurement (SSIM) is adopted to help judging the selection of the DM mode. The experimental results show that compared with the reference model VTM8.0, the proposed algorithm can reduce the coding time by 12.92% on average, and increases the BD-rate of Y, U, and V components by only 0.05%, 0.32%, and 0.29% respectively.

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Sub-Sampled Cross-Component Prediction for Chroma Component Coding

Cited by 19 publications

References 20 publications

Intra Prediction and Mode Coding in VVC

Intra Prediction and Mode Coding in VVC

Simplification on Cross-Component Linear Model in Versatile Video Coding

A Fast Chroma Intra-Prediction Mode Decision Algorithm Based on Texture Characteristics for VVC

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