# Recovering Sign Bits of DCT Coefficients in Digital Images as an Optimization Problem

arXiv: https://arxiv.org/abs/2211.01096 · HTML: https://mlsanity.com/paper/2211.01096/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 56 references that are in the map.
- First posted on arXiv 2 Nov 2022.

## Builds on (most cited first, 2 of 2)

- [Multi-Channel Deep Networks for Block-Based Image Compressive Sensing](https://mlsanity.com/paper/1908.11221/) (2019)
- [Compressing Sign Information in DCT-based Image Coding via Deep Sign Retrieval](https://mlsanity.com/paper/2209.10712/) (2022)

## Abstract (arXiv)

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.