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Computationally Efficient Neural Image Compression
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Image compression using neural networks have reached or exceeded non-neural methods (such as JPEG, WebP, BPG). While these networks are state of the art in ratedistortion performance, computational feasibility of these models remains a challenge. We apply automatic network optimization techniques to reduce the computational complexity of a popular architecture used in neural image compression, analyze the decoder complexity in execution runtime and explore the trade-offs between two distortion metrics, rate-distortion performance and run-time performance to design and research more computationally efficient neural image compression. We find that our method decreases the decoder run-time requirements by over 50% for a stateof-the-art neural architecture.
Forward citations
Cited by 2 Pith papers
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N-O Cool-chic: reconcile fast encoding with lightweight decoding for neural image compression
N-O Cool-chic replaces per-image overfitting with a shared analysis transform, reducing encoding complexity by ~1000x while keeping a 2,300 MAC/pixel decoder and only a 45% rate penalty versus overfitted Cool-chic.
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ABC: Adaptive BayesNet Structure Learning for Computational Scalable Multi-task Image Compression
ABC learns the structure of a neural image compression codec jointly with a rate-distortion-complexity objective, making the codec computationally scalable across the encoder, decoder, and autoregressive context model.
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