Recovering Sign Bits of DCT Coefficients in Digital Images as an Optimization Problem
This work addresses a specific challenge in digital image processing for applications such as JPEG encoding, but it is incremental as it builds on prior methods for solving MILP problems in this domain.
The paper tackled the problem of recovering sign bits in DCT coefficients of digital images, which is crucial for applications like image compression and encryption, by proposing two approximation methods for an NP-hard MILP problem. The experimental results demonstrated that these methods significantly outperformed existing approaches in both objective and subjective evaluations.
Recovering unknown, missing, damaged, distorted, or lost information in DCT coefficients is a common task in multiple applications of digital image processing, including image compression, selective image encryption, and image communication. This paper investigates the recovery of sign bits in DCT coefficients of digital images, by proposing two different approximation methods to solve a mixed integer linear programming (MILP) problem, which is NP-hard in general. One method is a relaxation of the MILP problem to a linear programming (LP) problem, and the other splits the original MILP problem into some smaller MILP problems and an LP problem. We considered how the proposed methods can be applied to JPEG-encoded images and conducted extensive experiments to validate their performances. The experimental results showed that the proposed methods outperformed other existing methods by a substantial margin, both according to objective quality metrics and our subjective evaluation.