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The Devil Is in the Details: Window-based Attention for Image Compression (2203.08450v1)

Published 16 Mar 2022 in eess.IV and cs.CV

Abstract: Learned image compression methods have exhibited superior rate-distortion performance than classical image compression standards. Most existing learned image compression models are based on Convolutional Neural Networks (CNNs). Despite great contributions, a main drawback of CNN based model is that its structure is not designed for capturing local redundancy, especially the non-repetitive textures, which severely affects the reconstruction quality. Therefore, how to make full use of both global structure and local texture becomes the core problem for learning-based image compression. Inspired by recent progresses of Vision Transformer (ViT) and Swin Transformer, we found that combining the local-aware attention mechanism with the global-related feature learning could meet the expectation in image compression. In this paper, we first extensively study the effects of multiple kinds of attention mechanisms for local features learning, then introduce a more straightforward yet effective window-based local attention block. The proposed window-based attention is very flexible which could work as a plug-and-play component to enhance CNN and Transformer models. Moreover, we propose a novel Symmetrical TransFormer (STF) framework with absolute transformer blocks in the down-sampling encoder and up-sampling decoder. Extensive experimental evaluations have shown that the proposed method is effective and outperforms the state-of-the-art methods. The code is publicly available at https://github.com/Googolxx/STF.

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Authors (3)
  1. Renjie Zou (2 papers)
  2. Chunfeng Song (11 papers)
  3. Zhaoxiang Zhang (162 papers)
Citations (164)
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Summary

Analyzing Window-based Attention for Image Compression

The paper "The Devil Is in the Details: Window-based Attention for Image Compression" investigates innovative methodologies in the domain of learned image compression, specifically focusing on improving the capture of local redundancy in image data to enhance reconstruction quality post-compression. The authors argue that while CNN-based architectures have historically made significant contributions to image compression, their structure inherently lacks the efficiency needed for capturing non-repetitive textures, thereby affecting the fidelity of the reconstructed images.

Key Contributions

  1. Window-based Local Attention: The paper proposes a shift towards window-based attention mechanisms rather than relying solely on global feature extraction, as traditionally done by CNNs. This local-focused attention aims to better capture fine-grained textures, which are crucial for improved image quality after decompression. The window-based local attention module functions as an adaptable component that can be integrated into existing CNN and Transformer models to enhance their performance.
  2. Symmetrical Transformer Framework: The introduction of a Symmetrical TransFormer (STF) framework represents another significant advancement. This framework includes absolute transformer blocks within both the down-sampling encoder and up-sampling decoder, standing as one of the pioneering efforts to tailor transformer architecture specifically for image compression tasks. The paper showcases that employing such a framework leads to favorable results when benchmarked against state-of-the-art methods.
  3. Comprehensive Studies and Experiments: Through rigorous experimentation, the paper evaluates various attention mechanisms, asserting the superiority of local-aware attention in texture reconstruction. This evaluation is thorough, spanning multiple datasets to ensure a robust assessment of the proposed techniques. The experimental results reveal that the window-based attention modules offer notable enhancements in rate-distortion (RD) performance metrics compared to existing models.
  4. Implications of Human Perception in Compression: The paper further discusses the alignment or lack thereof between human perceptual understanding and computational metrics such as PSNR and MS-SSIM, emphasizing a rate-distortion-perception trade-off important for real-world applications where visual quality is paramount.

Analytical Overview

The paper underscores the necessity of rethinking traditional CNN architectures that focus predominantly on global feature extraction. In doing so, the authors introduce a dual focus on capturing local textures through the window-based attention mechanism, thereby achieving finer detail in reconstructed images. The proposed STF framework also contributes to this advancement by leveraging the growing applicability of transformers in image processing tasks, ensuring not only competitive but enhanced RD performance.

Implications and Future Work

The implications of the authors' research are both promising and practical. By addressing local redundancy and employing window-based attention mechanisms, learned image compression can potentially yield superior visual fidelity, which is vital in applications where detail and texture significantly influence user experience. The research invites further exploration into more efficient attention mechanisms and normalization techniques in the context of transformer-based architectures, which could refine image compression processes even further.

Moreover, understanding and incorporating human perceptual measures into objective compression metrics opens the door to more perceptually aligned compression models, which could revolutionize various industries relying heavily on digital imaging. Future research directions could explore hybrid architectures combining CNN and transformer blocks to extract both global and local features effectively.

In conclusion, the paper provides substantial insights and contributions to the field of image compression, fostering advancements in both theoretical understanding and practical implementations of compression systems. The integration of window-based attention and symmetrical transformer enhancements presents a progressive approach that is poised to influence future studies and developments in AI-driven image processing.