Abstract
High-efficiency video coding (HEVC) effectively reduces the amount of video data while unavoidably introducing compression noise. The in-loop filter can enhance the reconstructed frames at the encoder to prevent compression noise from transmitting to the subsequent frames. The existing incorporated in-loop filters for HEVC do not fully combine the advantages of CNN and video coding priors. Moreover, obtaining the accuracy noise level is essential for adaptively in-loop filtering. To further enhance the compressed video quality in the encoder, we propose a nonlocal in-loop filter for HEVC using a CNN-based compression noise estimation network(CNEN). In the noise estimation part, we adopt a classification network to estimate the noise of compressed HEVC videos according to video content characteristics. In the denoising part, we propose a spatial-temporal nonlocal low-rank(STNLLR) prior by simultaneously exploiting the nonlocal self-similarity of video in spatial and temporal domains. We also propose an adaptive narrow quantization constraint (ANQC) prior by limiting the reconstructed pixel values adaptively according to the quantization parameters(QPs). The experimental results show that CNEN outperforms the existing compression noise estimation methods. Furthermore, our in-loop filter can improve the quality of the HEVC reconstructed frames under AI, LDP, and RA configurations, achieving an average reduction of 4.17%, 10.46%, and 6.10% in the Bjøntegaard Delta Bit Rate (BDBR), respectively.
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Acknowledgements
This work is supported by the National Natural Science Foundation of China (Grant No. 61871279 and GrantNo. 62081330105) and the Fundamental Research Funds for the Central Universities (Grant No. 2021SCU12061).
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Sun, W., He, X., Chen, H. et al. A nonlocal HEVC in-loop filter using CNN-based compression noise estimation. Appl Intell 52, 17810–17828 (2022). https://doi.org/10.1007/s10489-022-03259-z
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DOI: https://doi.org/10.1007/s10489-022-03259-z