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JDEC: JPEG Decoding via Enhanced Continuous Cosine Coefficient

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+ + + Woo Kyoung Han1,2, + + Sunghoon Im2, + + Jaedeok Kim3*, + + Kyoong Hwan Jin1* + +
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+ Korea University1, DGIST2, NVIDIA3
CVPR 2024 +
*Indicates Corresponding Authors +
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Abstract

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+ We propose a practical approach to JPEG image decoding, utilizing a local implicit neural representation with continuous cosine formulation. The JPEG algorithm significantly quantizes discrete cosine transform (DCT) spectra to achieve a high compression rate, inevitably resulting in quality degradation while encoding an image. We have designed a continuous cosine spectrum estimator to address the quality degradation issue that restores the distorted spectrum. By leveraging local DCT formulations, our network has the privilege to exploit dequantization and upsampling simultaneously. Our proposed model enables decoding compressed images directly across different quality factors using a single pre-trained model without relying on a conventional JPEG decoder. As a result, our proposed network achieves state-of-the-art performance in flexible color image JPEG artifact removal tasks.

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+ Instead of using a conventional JPEG decoder to refine the high-quality (HQ) image from the low-quality (LQ) image, our JDEC directly decodes the LQ spectrum by learning a continuous spectrum. +

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BibTeX

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@INPROCEEDINGS {jdec2024han,
+		author = {W. Han and S. Im and J. Kim and K. Jin},
+		booktitle = {2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
+		title = {{JDEC}: JPEG Decoding via Enhanced Continuous Cosine Coefficients},
+		year = {2024},
+		}
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