Recovering Sign Bits of DCT Coefficients in Digital Images as an Optimization Problem
원문 초록
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.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
GANCompress: GAN-Enhanced Neural Image Compression with Binary Spherical Quantization
Enhanced Diagnostic Fidelity in Pathology Whole Slide Image Compression via Deep Learning
FD-LSCIC: Frequency Decomposition-based Learned Screen Content Image Compression
Interleaved Block-based Learned Image Compression with Feature Enhancement and Quantization Error Compensation
Residual Prior-driven Frequency-aware Network for Image Fusion
Perception-Oriented Latent Coding for High-Performance Compressed Domain Semantic Inference
토론
이 논문과 연결된 포럼 글이 아직 없습니다.