5.4.2SCE4 primary contributions
JCTVC-M0058 SCE4.1.2: Inter-layer fixed refining filter [W. Zhang, L. Xu, Y. Han, Z. Deng, X. Cai, Y. Chiu (Intel)]
JCTVC-M0265 SCE4: Results of Test 4.2.1 on High Frequency Pass Inter Layer Sample Adaptive Offset Filter [W. Pu, J. Chen, X. Li, M. Karczewicz (Qualcomm)]
JCTVC-M0267 SCE4: Results of Test 4.2.2 on Adaptive Re-Sampling Filter [W. Pu, J. Chan, X. Li, M. Karczewicz (Qualcomm)]
JCTVC-M0195 SCE4.2.3: Inter-Layer Adaptive Filter on Reconstructed Base Layer Samples [M. Guo, S. Liu, S. Lei (MediaTek)]
JCTVC-M0183 SCE4: Results of test 4.2.4 on chroma enhancement for inter layer prediction [J. Dong, Y. Ye, Y. He (InterDigital)]
This contribution proposes to enhance the chroma planes for better inter-layer prediction, which is achieved by using the corresponding information from the luma plane. More specifically, for each chroma sample, an appropriate offset calculated based on the values of surrounding 3×4 luma samples is added. In the RefIdx framework, the average {Y, U, V} gains are {−0.8%, −7.5%, −9.1%}, {−0.3%, −9.2%, −9.7%}, {−0.2%, −6.4%, −7.2%}, and {−0.2%, −6.9%, −7.9%} for AI, RA, LDP, and LDB, respectively. Similar performance gain can be observed for the IntraBL framework. Compared with simulcast, the proposed chroma enhancement brings down the gap between luma gain and chroma gain significantly, which is large by only using SHM-1.0. The average memory access increase is from 0% to 4% for picture-based implementation, and 0% to 10% for block-based implementation. The average computational complexity (i.e., number of multiplications and additions) increase is from 14% to 37% for picture-based implementation, and 3% to 43% for block-based implementation. The proposed algorithm can be implemented using 16-bit integer arithmetic.
JCTVC-M0055 SCE4.2.5: Switchable De-ringing Filter for Inter-layer Prediction [Z. Ma, F. Fernandes]
JCTVC-M0213 SCE4.2.6: Adaptive up-sampling of base layer picture using bilateral filters [J. Zhao, K. Misra, A. Segall (Sharp)]
JCTVC-M0087 SCE4: De-noising filter for SNR scalability [E. Alshina, A. Alshin (Samsung)]
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