| Summary: | In recent years, video content has become a significant contributor to Internet traffic, prompting the development of efficient codecs, such as High Efficiency Video Coding (HEVC) and Versatile Video Coding (VVC), to reduce bandwidth usage and storage requirements. However, these video coding standards still exhibit quality degradation and artifacts in the decoded frames. To address this issue, researchers have introduced several network architectures based on deep-learning algorithms; however, most of them focus on in-loop filtering, which requires additional bits to transmit filter information from the encoder to the decoder under a video-coding framework. In this paper, we propose a neural-network-based post-processing method to enhance the decoded frames. In the experimental result, the proposed model achieves a significant bitrate reduction, as measured by Bjøntegaard Delta of 4.54%, 4.13%, and 5.21% for random access (RA), low-delay (LD), and all-intra (AI) configurations, respectively, while also improving peak signal-to-noise ratio (PSNR).
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