Recovering Missing Coefficients in DCT-Transformed Images
This work addresses a domain-specific issue for image processing and encryption, with incremental improvements in recovery methods.
The paper tackles the problem of recovering missing DCT coefficients in images by modeling it as an optimization problem solved via linear programming, achieving surprisingly good visual quality even with over 10 missing coefficients and outperforming existing methods when only the DC coefficient is missing.
A general method for recovering missing DCT coefficients in DCT-transformed images is presented in this work. We model the DCT coefficients recovery problem as an optimization problem and recover all missing DCT coefficients via linear programming. The visual quality of the recovered image gradually decreases as the number of missing DCT coefficients increases. For some images, the quality is surprisingly good even when more than 10 most significant DCT coefficients are missing. When only the DC coefficient is missing, the proposed algorithm outperforms existing methods according to experimental results conducted on 200 test images. The proposed recovery method can be used for cryptanalysis of DCT based selective encryption schemes and other applications.