Expanded Adaptive Scaling Normalization for End to End Image Compression

Shin, Chajin; Lee, Hyeongmin; Son, Hanbin; Lee, Sangjin; Lee, Dogyoon; Lee, Sangyoun

doi:10.1007/978-3-031-19790-1_24

Cited by 6 publications

(1 citation statement)

References 15 publications

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“…Some works focus on the transform, e.g., generalized divisive normalization (GDN) Simoncelli 2016a,b, 2017), residual block (Theis et al 2017), attention module (Cheng et al 2020;Zhou et al 2019), non-local attention module (Chen et al 2021), attentional multi-scale back projection (Gao et al 2021), window attention module (Zou, Song, and Zhang 2022), stereo attention module (Wödlinger et al 2022), and expanded adaptive scaling normalization (EASN) (Shin et al 2022) have been used to improve the nonlinear transform. Invertible neural networkbased architecture (Cai et al 2022;Helminger et al 2021;Ho et al 2021;Ma et al 2019Ma et al , 2022aXie, Cheng, and Chen 2021) and transformer-based architecture (Qian et al 2022;Zhu, Yang, and Cohen 2022;Zou, Song, and Zhang 2022;Liu, Sun, and Katto 2023) also have been utilized to enhance the modeling capacity of the transforms.…”

Section: Related Workmentioning

confidence: 99%

Make Lossy Compression Meaningful for Low-Light Images

Cai,

Chen,

Zhong

et al. 2024

AAAI

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Low-light images frequently occur due to unavoidable environmental influences or technical limitations, such as insufficient lighting or limited exposure time. To achieve better visibility for visual perception, low-light image enhancement is usually adopted. Besides, lossy image compression is vital for meeting the requirements of storage and transmission in computer vision applications. To touch the above two practical demands, current solutions can be categorized into two sequential manners: ``Compress before Enhance (CbE)'' or ``Enhance before Compress (EbC)''. However, both of them are not suitable since: (1) Error accumulation in the individual models plagues sequential solutions. Especially, once low-light images are compressed by existing general lossy image compression approaches, useful information (e.g., texture details) would be lost resulting in a dramatic performance decrease in low-light image enhancement. (2) Due to the intermediate process, the sequential solution introduces an additional burden resulting in low efficiency. We propose a novel joint solution to simultaneously achieve a high compression rate and good enhancement performance for low-light images with much lower computational cost and fewer model parameters. We design an end-to-end trainable architecture, which includes the main enhancement branch and the signal-to-noise ratio (SNR) aware branch. Experimental results show that our proposed joint solution achieves a significant improvement over different combinations of existing state-of-the-art sequential ``Compress before Enhance'' or ``Enhance before Compress'' solutions for low-light images, which would make lossy low-light image compression more meaningful. The project is publicly available at: https://github.com/CaiShilv/Joint-IC-LL.

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