In Versatile Video Coding (VVC), Cross-component Linear Model (CCLM) predicts chroma samples by assuming a linear relationship between luma and chroma components. In performing CCLM for video in YUV 4:2:0 chroma format, collocated luma samples are firstly downsampled by a low-pass filter to match luma resolution with chroma, and one linear model of luma-chroma sample pairs is applied on the reconstructed luma samples to generate the predicted chroma samples. However, the low-pass downsampling procedure ignores relative spatial variations among luma samples in proximity, such as edge and gradient information. To solve this issue, a new coding technique, namely gradient linear model (GLM), is proposed for further compression efficiency exploration beyond VVC. Instead of using a low-pass filter in CCLM, the GLM utilizes high-pass gradient filters to generate the downsampled luma values. In this paper, two GLM schemes are provided with different trade-offs between coding gain and complexity, including: 1) a 2-parameter scheme that shares the CCLM module framework but replaces the downsampling filter with high-pass gradient filters; 2) a 3-parameter scheme that further combines the luma gradients with the low-pass downsampled luma values. Based on the enhanced compression model (ECM-5.0) software from the joint video experts team (JVET), simulation results show that the 2-parameter GLM achieves average Bjontegaard delta-rate (BD-rate) savings of {1.01%, 1.66%, 1.81%} and {0.69%, 0.95%, 1.12%} for {Y, U, V} components under the All Intra and Random Access configurations, respectively, and the 3-parameter GLM provides {1.28%, 3.23%, 3.28%} and {0.92%, 2.19%, 2.26%} BD-rate savings for {Y, U, V} components under the All Intra and Random Access configurations, respectively. Both of the proposed GLM schemes have been adopted to the ECM software platform.

This paper proposes one new In-loop filtering technique cross-component sample adaptive offset (CCSAO) for further coding efficiency improvement beyond Versatile Video Coding (VVC). The CCSAO reduces the sample distortion by 1) utilizing the strong correlation between luma and chroma components to classify the reconstructed samples into different categories and 2) deriving one offset for each category and adding the offset to the samples in the category. The offset of each category is properly derived at encoder and signaled to decoder. To keep the design at low complexity, only band information of reconstructed samples is considered for the sample classification of the CCSAO. To verify the performance, the proposed CCSAO is implemented on top of the enhanced compression model (ECM) for the joint video exploration team (JVET)'s exploratory work of future video coding technologies beyond VVC. Simulation results show that the CCSAO achieves average {0.20%, 2.83%, 2.98%} and {0.41%, 7.36%, 7.36%} Bjentegaard delta (BD)-rate savings for {Y, U, V} components under the Random Access and Low Delay B configuration, with negligible complexity impacts on encoding and decoding complexity. The proposed CCSAO scheme has been adopted to the ECM-2.0 software platform.

This paper describes a scalable video codec that was submitted as a response to the joint call for proposals issued by ISO/IEC MPEG and ITU-T VCEG on HEVC scalable extension. The proposed codec uses a multi-loop decoding structure. Several inter-layer texture prediction methods are employed to remove the inter-layer redundancy. Inter-layer prediction is also used when coding enhancement layer syntax elements such as motion parameter and intra prediction mode, to further reduce bit overhead. Additionally, alternative transforms as well as adaptive coefficients scanning are used to code the prediction residues more efficiently. Experimental results are presented to demonstrate the effectiveness of the proposed scheme. When compared to HEVC single-layer coding, the additional rate overhead for the proposed scalable extension is 1.2% to 6.4% to achieve two layers of SNR and spatial scalability.