通过挖掘DCT系数相关性，提升JPEG图像增强效果与效率
Uncover Treasures in DCT: Advancing JPEG Quality Enhancement by Exploiting Latent Correlations
- 发现DCT系数中两类关键相关性，用于指导图像增强
- 相比像素域方法，峰值信噪比提升0.35 dB，处理速度提高60.5%
- 可将主流像素域模型轻松迁移至DCT域，降低计算开销
联合图像专家组（JPEG）通过量化离散余弦变换（DCT）系数实现数据压缩，但会引入压缩伪影。现有JPEG质量增强方法多在像素域进行，需解码带来高计算成本。因此，直接在DCT域进行增强受到关注。然而，当前方法性能有限。本文识别出JPEG图像DCT系数中的两类关键相关性，并基于此提出高级DCT域JPEG质量增强方法（AJQE），充分挖掘这些相关性。该方法使众多成熟的像素域模型可迁移至DCT域，在保持高性能的同时显著降低计算复杂度。实验表明，与像素域模型相比，本方法在平均上实现PSNR提升0.35 dB，增强吞吐量提高60.5%。
原文摘要 · Abstract (English)
Joint Photographic Experts Group (JPEG) achieves data compression by quantizing Discrete Cosine Transform (DCT) coefficients, which inevitably introduces compression artifacts. Most existing JPEG quality enhancement methods operate in the pixel domain, suffering from the high computational costs of decoding. Consequently, direct enhancement of JPEG images in the DCT domain has gained increasing attention. However, current DCT-domain methods often exhibit limited performance. To address this challenge, we identify two critical types of correlations within the DCT coefficients of JPEG images. Building on this insight, we propose an Advanced DCT-domain JPEG Quality Enhancement (AJQE) method that fully exploits these correlations. The AJQE method enables the adaptation of numerous well-established pixel-domain models to the DCT domain, achieving superior performance with reduced computational complexity. Compared to the pixel-domain counterparts, the DCT-domain models derived by our method demonstrate a 0.35 dB improvement in PSNR and a 60.5% increase in enhancement throughput on average.
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