Implicit Dual-Domain Convolutional Network for Robust Color Image Compression Artifact Reduction

Boundary Artifact Reduction in Wavelet Image Compression

In our contribution, we compute new edge wavelet filters for image compression and we compare errors for several image compression techniques. Our numerical experiments confirm the theoretical assumption that the error of the compression is smaller for the discrete wavelet transform with derived edge filters than for other methods using an extension of an image.

Artifact reduction with diffusion preprocessing for image compression

Tamás Szirányi Hungarian Academy of Sciences Analogical and Neural Computing Laboratory Computer and Automation Instrumentation H-1111 Budapest Kende u. 13-17 Hungary and University of Veszprém Department of Image Processing and Neurocomputing H-8200 Veszprém Egyetem u. 10, Hungary E-mail: [email protected] Abstract. We evaluate a preprocessing method for image compression artifact reduction b...

A Radon-based Convolutional Neural Network for Medical Image Retrieval

Image classification and retrieval systems have gained more attention because of easier access to high-tech medical imaging. However, the lack of availability of large-scaled balanced labelled data in medicine is still a challenge. Simplicity, practicality, efficiency, and effectiveness are the main targets in medical domain. To achieve these goals, Radon transformation, which is a well-known t...

Artifact reduction in low bit rate DCT-based image compression

This correspondence presents a scheme for artifact reduction of low bit rate discrete-cosine-transform-compressed (DCT-compressed) images. First, the DC coefficients are calibrated using gradient continuity constraints. Then, an improved Huber-Markov-random-field-based (HMRF-based) smoothing is applied. The constrained optimization is implemented by the iterative conditional mode (ICM). Final r...

Building Dual-Domain Representations for Compression Artifacts Reduction