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Entroformer: A Transformer-based Entropy Model for Learned Image Compression

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arxiv 2202.05492 v2 pith:KBL5H57N submitted 2022-02-11 eess.IV cs.CV

Yichen Qian , Ming Lin , Xiuyu Sun , Zhiyu Tan , Rong Jin This is my paper
classification eess.IVcs.CV
keywords imagecompressionentroformerentropymodeldecodingdependenciesdistribution
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One critical component in lossy deep image compression is the entropy model, which predicts the probability distribution of the quantized latent representation in the encoding and decoding modules. Previous works build entropy models upon convolutional neural networks which are inefficient in capturing global dependencies. In this work, we propose a novel transformer-based entropy model, termed Entroformer, to capture long-range dependencies in probability distribution estimation effectively and efficiently. Different from vision transformers in image classification, the Entroformer is highly optimized for image compression, including a top-k self-attention and a diamond relative position encoding. Meanwhile, we further expand this architecture with a parallel bidirectional context model to speed up the decoding process. The experiments show that the Entroformer achieves state-of-the-art performance on image compression while being time-efficient.

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Cited by 4 Pith papers

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

  1. LANCE: Locally Adaptive Neural Context Estimation for Overfitted Image Compression

    eess.IV 2026-05 unverdicted novelty 6.0 of 10

    LANCE extends OIC frameworks with a spatial hyperprior and predictive coding scheme, reporting BD-rate gains of 1.4-3% over Cool-Chic 4.0 on Kodak and CLIC.

  2. StableCodec: Taming One-Step Diffusion for Extreme Image Compression

    eess.IV 2025-06 conditional novelty 6.0 of 10

    A one-step diffusion codec that compresses noisy latents at 64x and decodes with a single denoising step, setting state-of-the-art FID, KID, and DISTS at ultra-low bitrates.

  3. Generative Image Compression by Estimating Gradients of the Rate-variable Feature Distribution

    eess.IV 2025-05 conditional novelty 6.0 of 10

    A single rate-variable generative compression model treats quantization as a forward corruption and reverses it with a two-step denoiser, outperforming prior generative codecs on perceptual quality benchmarks.

  4. Spectral and Spatial Graph Learning for Multispectral Solar Image Compression

    cs.CV 2025-12 conditional novelty 5.0 of 10

    A graph-based learned codec modeling wavelength-to-wavelength relationships plus windowed spatial attention reports modest PSNR/MS-SSIM gains and 20.15% lower MSID on six-channel solar images than two self-defined baselines.

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