Spatially adaptive high-resolution image reconstruction of DCT-based compressed images

Sung Cheol Park*, Moon Gi Kang, C. Andrew Segall, Aggelos K. Katsaggelos

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

19 Scopus citations


The problem of recovering a high-resolution image from a sequence of low-resolution DCT-based compressed observations is considered in this paper. The introduction of compression complicates the recovery problem. We analyze the DCT quantization noise and propose to model it in the spatial domain as a colored Gaussian process. This allows us to estimate the quantization noise at low bit-rates without explicit knowledge of the original image frame, and we propose a method that simultaneously estimates the quantization noise along with the high resolution data. We also incorporate a nonstationary image prior model to address blocking and ringing artifacts while still preserving edges. To facilitate the simultaneous estimate, we employ a regularization functional to determine the regularization parameter without any prior knowledge of the reconstruction procedure. The smoothing functional to be minimized is then formulated to have a global minimizer in spite of its nonlinearity by enforcing convergence and convexity requirements. Experiments illustrate the benefit of the proposed method when compared to traditional high-resolution image reconstruction methods. Quantitative and qualitative comparisons are provided.

Original languageEnglish (US)
Pages (from-to)573-585
Number of pages13
JournalIEEE Transactions on Image Processing
Issue number4
StatePublished - Apr 2004


  • DCT-based compression
  • High-resolution image reconstruction
  • Quantization noise
  • Regularization

ASJC Scopus subject areas

  • Software
  • Computer Graphics and Computer-Aided Design


Dive into the research topics of 'Spatially adaptive high-resolution image reconstruction of DCT-based compressed images'. Together they form a unique fingerprint.

Cite this