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Non-local Attention Optimized Deep Image Compression

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arxiv 1904.09757 v1 pith:S5H2KIUR submitted 2019-04-22 eess.IV cs.CV

Haojie Liu , Tong Chen , Peiyao Guo , Qiu Shen , Xun Cao , Yao Wang , Zhan Ma This is my paper · ORCID
classification eess.IVcs.CV
keywords imageattentioncompressionfeaturesnon-localdeepframeworkhyperprior
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This paper proposes a novel Non-Local Attention Optimized Deep Image Compression (NLAIC) framework, which is built on top of the popular variational auto-encoder (VAE) structure. Our NLAIC framework embeds non-local operations in the encoders and decoders for both image and latent feature probability information (known as hyperprior) to capture both local and global correlations, and apply attention mechanism to generate masks that are used to weigh the features for the image and hyperprior, which implicitly adapt bit allocation for different features based on their importance. Furthermore, both hyperpriors and spatial-channel neighbors of the latent features are used to improve entropy coding. The proposed model outperforms the existing methods on Kodak dataset, including learned (e.g., Balle2019, Balle2018) and conventional (e.g., BPG, JPEG2000, JPEG) image compression methods, for both PSNR and MS-SSIM distortion metrics.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Point Cloud-Assisted Neural Image Compression

    eess.IV 2024-12 conditional novelty 6.0 of 10

    Point cloud depth projected onto the image, fused through a new attention module, improves learned image compression on KITTI by 54.5% BD-rate over Cheng2020.

  2. Efficient Progressive Image Compression with Variance-aware Masking

    cs.CV 2024-11 conditional novelty 6.0 of 10

    A progressive image codec that ranks residual latent elements by predicted standard deviation and transmits them from most to least important, matching state-of-the-art RD with far lower decode cost.

  3. An Information-Theoretic Regularizer for Lossy Neural Image Compression

    cs.CV 2024-11 conditional novelty 5.0 of 10

    A regularizer that maximizes conditional source entropy gives BD-rate gains of 0.9% to 3.0% across five neural compression models, but may be equivalent to reweighting the rate term.

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