JDEC: JPEG Decoding via Enhanced Continuous Cosine Coefficients
Woo Kyoung Han Sunghoon Im Jaedeok Kim Kyong Hwan Jin
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. Our source
code is available at https://github.com/WooKyoungHan/JDEC.