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End-to-End Learnt Image Compression via Non-Local Attention Optimization and Improved Context Modeling.

Tong Chen, Haojie Liu, Zhan Ma

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
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    This study introduces NLAIC, a novel deep learning image compression method. It achieves state-of-the-art compression efficiency using non-local attention and improved context modeling for better image quality.

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    Area of Science:

    • Computer Vision
    • Machine Learning
    • Image Processing

    Background:

    • Lossy image compression is crucial for efficient data storage and transmission.
    • Existing methods often face trade-offs between compression ratio and image quality.
    • Deep learning approaches offer potential for improved compression performance.

    Purpose of the Study:

    • To propose an end-to-end learned lossy image compression method.
    • To enhance compression efficiency and image quality using advanced deep neural network techniques.
    • To develop a practical and computationally efficient compression model.

    Main Methods:

    • Utilizes a deep neural network (DNN)-based variational auto-encoder (VAE) structure.
    • Incorporates Non-Local Attention optimization and Improved Context modeling (NLAIC).
    • Employs non-local network operations for feature extraction and attention mechanisms for adaptive bit allocation.

    Main Results:

    • The NLAIC model achieves state-of-the-art compression efficiency on standard datasets (Kodak, Tecnick).
    • Demonstrates superior performance in both Peak Signal-to-Noise Ratio (PSNR) and Multi-Scale Structural Similarity (MS-SSIM) metrics.
    • Outperforms existing learned and conventional compression methods (BPG, JPEG2000, JPEG).

    Conclusions:

    • NLAIC offers a highly efficient and effective learned lossy image compression solution.
    • The method provides a unified model for variable rates without re-training, enhancing practicality.
    • Publicly accessible materials promote reproducible research in image compression.