Learning to Improve Image Compression Without Changing the Standard Decoder
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Date
2020
Publication Type
Conference Paper
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yes
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Abstract
In recent years we have witnessed an increasing interest in applying Deep Neural Networks (DNNs) to improve the rate-distortion performance in image compression. However, the existing approaches either train a post-processing DNN on the decoder side, or propose learning for image compression in an end-to-end manner. This way, the trained DNNs are required in the decoder, leading to the incompatibility to the standard image decoders (e.g., JPEG) in personal computers and mobiles. Therefore, we propose learning to improve the encoding performance with the standard decoder. In this paper, We work on JPEG as an example. Specifically, a frequency-domain pre-editing method is proposed to optimize the distribution of DCT coefficients, aiming at facilitating the JPEG compression. Moreover, we propose learning the JPEG quantization table jointly with the pre-editing network. Most importantly, we do not modify the JPEG decoder and therefore our approach is applicable when viewing images with the widely used standard JPEG decoder. The experiments validate that our approach successfully improves the rate-distortion performance of JPEG in terms of various quality metrics, such as PSNR, MS-SSIM and LPIPS. Visually, this translates to better overall color retention especially when strong compression is applied.
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Publication status
published
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Book title
Computer Vision – ECCV 2020 Workshops Glasgow, UK, August 23–28, 2020, Proceedings, Part IV
Journal / series
Volume
12538
Pages / Article No.
200 - 216
Publisher
Springer
Event
16th European Conference on Computer Vision (ECCV 2020) (virtual)
Edition / version
Methods
Software
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Subject
Organisational unit
03514 - Van Gool, Luc (emeritus) / Van Gool, Luc (emeritus)
Notes
Due to the Coronavirus (COVID-19) the conference was conducted virtually.