ChWDTA: Channel-wise Wavelet-Domain Transformer Attention and Entropy Modeling for Learned Image Compression

• URL: http://arxiv.org/abs/2606.00111v1
• Date: Wed, 27 May 2026 04:36:08 GMT
• Title: ChWDTA: Channel-wise Wavelet-Domain Transformer Attention and Entropy Modeling for Learned Image Compression
• Abstract summary: We introduce channel-wise wavelet transforms into both the transformer and entropy-coding components.<n>With this configuration, the proposed scheme obtains BD-rate reductions of -17.82%, -19.15%, and -22.56% on the Kodak, CLIC Professional Validation, and Tecnick test sets.
• Score: 36.75128193748412
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 attention output back with the inverse transform. The resulting Channel-wise Wavelet-Domain Transformer Block (ChWDTB) therefore preserves the spatial tokenization pattern of windowed attention while sparsifying the channel covariance seen by the attention projections. Second, in the entropy-coding stage, we introduce a channel-wise wavelet packet (ChWP) decomposition that produces four equal-sized subbands, which better fit channel-wise slice-based autoregressive entropy modeling. When each channel-wise subband is divided into two slices, we use eight slices for entropy coding. With this configuration, the proposed scheme obtains BD-rate reductions of -17.82%, -19.15%, and -22.56% on the Kodak, CLIC Professional Validation, and Tecnick test sets, respectively. Even when each channel-wise subband is coded as a single slice, the scheme still retains most of the coding gains with lower complexity. The results confirm the advantage of introducing wavelet transform in CNN-transformer-based LIC schemes.

Related papers

• Freqformer: Image-Demoiréing Transformer via Efficient Frequency Decomposition [83.40450475728792]
  We present Freqformer, a Transformer-based framework specifically designed for image demoir'eing through targeted frequency separation.<n>Our method performs an effective frequency decomposition that explicitly splits moir'e patterns into high-frequency spatially-localized textures and low-frequency scale-robust color distortions.<n>Experiments on various demoir'eing benchmarks demonstrate that Freqformer achieves state-of-the-art performance with a compact model size.
  arXiv  Detail & Related papers  (2025-05-25T12:23:10Z)
• Window-based Channel Attention for Wavelet-enhanced Learned Image Compression [18.13637383168527]
  We introduce window partition into channel attention for the first time to obtain large receptive fields and capture more global information.<n>Experiment results demonstrate that our method achieves state-of-the-art performances, reducing BD-rate by 18.54%, 23.98%, 22.33%, and 24.71% on four standard datasets.
  arXiv  Detail & Related papers  (2024-09-21T10:08:52Z)
• A Hybrid Transformer-Mamba Network for Single Image Deraining [70.64069487982916]
  Existing deraining Transformers employ self-attention mechanisms with fixed-range windows or along channel dimensions. We introduce a novel dual-branch hybrid Transformer-Mamba network, denoted as TransMamba, aimed at effectively capturing long-range rain-related dependencies.
  arXiv  Detail & Related papers  (2024-08-31T10:03:19Z)
• Bi-Level Spatial and Channel-aware Transformer for Learned Image Compression [0.0]
  We propose a novel Transformer-based image compression method that enhances the transformation stage by considering frequency components within the feature map. Our method integrates a novel Hybrid Spatial-Channel Attention Transformer Block (HSCATB), where a spatial-based branch independently handles high and low frequencies. We also introduce a Mixed Local-Global Feed Forward Network (MLGFFN) within the Transformer block to enhance the extraction of diverse and rich information.
  arXiv  Detail & Related papers  (2024-08-07T15:35:25Z)
• Frequency-Aware Transformer for Learned Image Compression [64.28698450919647]
  We propose a frequency-aware transformer (FAT) block that for the first time achieves multiscale directional ananlysis for Learned Image Compression (LIC)<n>The FAT block comprises frequency-decomposition window attention (FDWA) modules to capture multiscale and directional frequency components of natural images.<n>We also introduce frequency-modulation feed-forward network (FMFFN) to adaptively modulate different frequency components, improving rate-distortion performance.
  arXiv  Detail & Related papers  (2023-10-25T05:59:25Z)
• Dual Aggregation Transformer for Image Super-Resolution [92.41781921611646]
  We propose a novel Transformer model, Dual Aggregation Transformer, for image SR. Our DAT aggregates features across spatial and channel dimensions, in the inter-block and intra-block dual manner. Our experiments show that our DAT surpasses current methods.
  arXiv  Detail & Related papers  (2023-08-07T07:39:39Z)
• Learned Image Compression with Mixed Transformer-CNN Architectures [21.53261818914534]
  We propose an efficient parallel Transformer-CNN Mixture (TCM) block with a controllable complexity. Inspired by the recent progress of entropy estimation models and attention modules, we propose a channel-wise entropy model with parameter-efficient swin-transformer-based attention. Experimental results demonstrate our proposed method achieves state-of-the-art rate-distortion performances.
  arXiv  Detail & Related papers  (2023-03-27T08:19:01Z)
• WaveNets: Wavelet Channel Attention Networks [9.842943431749191]
  We propose Wavelet transform compression as a solution to the channel representation problem. We generalize channel attention using Wavelet compression and name it WaveNet. Our method outperforms the baseline SENet, and achieves the state-of-the-art results.
  arXiv  Detail & Related papers  (2022-11-04T18:26:47Z)
• Wavelet Channel Attention Module with a Fusion Network for Single Image Deraining [46.62290347397139]
  Single image deraining is a crucial problem because rain severely degenerates the visibility of images. We propose the new convolutional neural network (CNN) called the wavelet channel attention module with a fusion network.
  arXiv  Detail & Related papers  (2020-07-17T18:06:13Z)
• Channel-Level Variable Quantization Network for Deep Image Compression [50.3174629451739]
  We propose a channel-level variable quantization network to dynamically allocate more convolutions for significant channels and withdraws for negligible channels. Our method achieves superior performance and can produce much better visual reconstructions.
  arXiv  Detail & Related papers  (2020-07-15T07:20:39Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.