Channel-wise wavelet-domain transformer attention plus wavelet-packet entropy modeling yields BD-rate reductions of 17.8-22.6% on Kodak, CLIC, and Tecnick relative to prior LIC baselines.
Cool-chic 5.0: Faster Encoding and Inter-Feature Entropy Modeling for Overfitted Image Compression
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abstract
Overfitted codecs compress an image by learning a decoder tailored to the content during the encoding. As such, they trade increased encoding complexity for strong compression performance and low decoding complexity. This work introduces Cool-chic 5.0, the latest version in the Cool-chic series of overfitted codecs, featuring an updated decoder architecture and an improved optimization process. Cool-chic 5.0 outperforms all overfitted codecs with 10 times less encoding iterations. It offers -11% rate reduction compared to the state-of-the-art conventional codec H.266/VVC. It is also competitive with modern autoencoders such as MLIC++ while featuring a decoding complexity 250 times lower. This work is made open-source at https://github.com/Orange-OpenSource/Cool-Chic.
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ChWDTA: Channel-wise Wavelet-Domain Transformer Attention and Entropy Modeling for Learned Image Compression
Channel-wise wavelet-domain transformer attention plus wavelet-packet entropy modeling yields BD-rate reductions of 17.8-22.6% on Kodak, CLIC, and Tecnick relative to prior LIC baselines.