ChWDTA: Channel-wise Wavelet-Domain Transformer Attention and Entropy Modeling for Learned Image Compression
arXiv:2606.00111v1 Announce Type: cross Abstract: State-of-the-art learned image compression (LIC) schemes are increasingly based on hybrid CNN-transformer architectures. To further improve rate-distortion performance, we introduce channel-wise wavelet transforms into both the transformer and entropy-coding components. First, we propose a channel-wise wavelet-domain transformer attention (ChWDTA) mechanism. ChWDTA keeps the efficient windowed spatial self-attention used in modern LIC backbones, but computes the Q/K/V projections on channel-wise wavelet-transformed features before mapping the a
The paper presents a novel approach in image compression using hybrid CNN-transformer architectures, building on existing state-of-the-art methods.
Improved image compression directly impacts data transfer efficiency, storage costs, and the performance of AI systems handling visual data, potentially reducing computational load and energy consumption.
The proposed ChWDTA mechanism and entropy modeling could lead to more efficient learned image compression schemes, enhancing rate-distortion performance.
- · AI compute infrastructure providers
- · Digital media platforms
- · Cloud storage providers
- · AI/ML researchers
More efficient image compression algorithms will reduce data bandwidth requirements for visual information.
Lower data transfer and storage costs could accelerate the deployment of high-resolution AI vision systems in various applications.
Reduced computational and energy demands for image processing might marginally alleviate the compute supply chain and energy bottleneck for AI workloads.
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Read at arXiv cs.LG