JPEG Quantized Coefficient Recovery via DCT Domain Spatial-Frequential
  Transformer
Mingyu Ouyang Zhenzhong Chen
JPEG compression adopts the quantization of Discrete Cosine Transform (DCT)
coefficients for effective bit-rate reduction, whilst the quantization could
lead to a significant loss of important image details. Recovering compressed
JPEG images in the frequency domain has attracted more and more attention
recently, in addition to numerous restoration approaches developed in the pixel
domain. However, the current DCT domain methods typically suffer from limited
effectiveness in handling a wide range of compression quality factors, or fall
short in recovering sparse quantized coefficients and the components across
different colorspace. To address these challenges, we propose a DCT domain
spatial-frequential Transformer, named as DCTransformer. Specifically, a
dual-branch architecture is designed to capture both spatial and frequential
correlations within the collocated DCT coefficients. Moreover, we incorporate
the operation of quantization matrix embedding, which effectively allows our
single model to handle a wide range of quality factors, and a
luminance-chrominance alignment head that produces a unified feature map to
align different-sized luminance and chrominance components. Our proposed
DCTransformer outperforms the current state-of-the-art JPEG artifact removal
techniques, as demonstrated by our extensive experiments.