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Asymmetric Learned Image Compression with Multi-Scale Residual Block, Importance Map, and Post-Quantization Filtering. (arXiv:2206.10618v1 [eess.IV])
cs.LG updates on arXiv.org arxiv.org
Recently, deep learning-based image compression has made signifcant
progresses, and has achieved better ratedistortion (R-D) performance than the
latest traditional method, H.266/VVC, in both subjective metric and the more
challenging objective metric. However, a major problem is that many leading
learned schemes cannot maintain a good trade-off between performance and
complexity. In this paper, we propose an effcient and effective image coding
framework, which achieves similar R-D performance with lower complexity than
the state of the art. First, we develop …
arxiv compression filtering image importance map quantization scale