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.
Mlic++: Linear com- plexity multi-reference entropy modeling for learned image compression.arXiv preprint arXiv:2307.15421
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RDVQ enables joint rate-distortion optimization for vector-quantized generative image compression via differentiable codebook distribution relaxation and an autoregressive entropy model.
Introduces a benchmark for VLMs on compressed images and a universal adaptor to improve performance across codecs and bitrates.
A practical learned image codec delivers 2.3-3x bitrate savings over AV1/VVC and 20-40% over prior learned codecs while encoding 12MP images in 230ms on iPhone.
citing papers explorer
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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.
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Differentiable Vector Quantization for Rate-Distortion Optimization of Generative Image Compression
RDVQ enables joint rate-distortion optimization for vector-quantized generative image compression via differentiable codebook distribution relaxation and an autoregressive entropy model.
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Benchmarking and Enhancing VLM for Compressed Image Understanding
Introduces a benchmark for VLMs on compressed images and a universal adaptor to improve performance across codecs and bitrates.
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What Matters in Practical Learned Image Compression
A practical learned image codec delivers 2.3-3x bitrate savings over AV1/VVC and 20-40% over prior learned codecs while encoding 12MP images in 230ms on iPhone.