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Video Enhancement Network Based on CNN and Transformer
YUAN Lang, HUI Chen, WU Yanfeng, LIAO Ronghua, JIANG Feng, GAO Ying
ZTE Communications    2024, 22 (4): 78-88.   DOI: 10.12142/ZTECOM.202404011
Abstract33)   HTML0)    PDF (1179KB)(12)       Save

To enhance the video quality after encoding and decoding in video compression, a video quality enhancement framework is proposed based on local and non-local priors in this paper. Low-level features are first extracted through a single convolution layer and then processed by several conv-tran blocks (CTB) to extract high-level features, which are ultimately transformed into a residual image. The final reconstructed video frame is obtained by performing an element-wise addition of the residual image and the original lossy video frame. Experiments show that the proposed Conv-Tran Network (CTN) model effectively recovers the quality loss caused by Versatile Video Coding (VVC) and further improves VVC's performance.

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