Special Issue "New and Specialized Methods of Image Compression"

A special issue of Journal of Imaging (ISSN 2313-433X). This special issue belongs to the section "Image and Video Processing".

Deadline for manuscript submissions: 15 December 2021.

Special Issue Editor

Prof. Dr. Roman Starosolski
E-Mail Website
Guest Editor
Faculty of Automatic Control, Electronics and Computer Science, Silesian University of Technology, Akademicka 16, 44-100 Gliwice, Poland
Interests: image compression; data compression; image processing; biomedical imaging; image compression standards; lifting-based reversible transforms (color space transforms and DWT); reversible denoising and lifting steps; adaptive algorithms

Special Issue Information

Dear Colleagues,

The most dynamic period in the development of image compression methods was at the turn of the century, when such algorithms were created as, for example, JPEG200, which so far has not had a worthy successor. Since then, several new image compression methods and algorithms have been proposed as well as certain categories of images previously considered exotic have become popular and now are demanding efficient compression.

The purpose of this Special Issue “New and Specialized Methods of Image Compression” is to provide a broad and current overview of new developments in the image compression domain. The focus is placed on promising image compression methods targeted at both typical (photographic) images and other image types that are increasingly used today. We especially look forward to contributions of research and overview papers on:

* New image compression methods, including (but not limited to):

  • compression based on neural networks, convolutional networks, and deep learning;
  • employment of minimum rate predictors;
  • inpainting-based image compression;
  • new transforms for image compression and adaptive and hybrid transforms; and
  • the use of video coding algorithms for the compression of still images.

* Coding of special types of images, such as

  • screen content images;
  • images with a reduced number of colors;
  • medical image modalities, including multimodal and volumetric images;
  • raw camera sensor images (e.g., Bayer pattern);
  • multispectral and hyperspectral images, satellite images; and
  • light field images.

* Older promising techniques that have fallen out of the mainstream interest are of interest if possibly effective in conjunction with recent techniques or for special image types (like the use of fractal coding, Burrows–Wheeler transform, and histogram packing in image compression).

Prof. Dr. Roman Starosolski
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Imaging is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • deep learning-based image compression
  • minimum rate predictors
  • inpainting-based image compression
  • fractal image coding
  • adaptive and hybrid transforms
  • screen content coding
  • multimodal and volumetric medical images
  • raw camera sensor images
  • multispectral and hyperspectral images
  • satellite images
  • light field image coding

Published Papers (2 papers)

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Research

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Article
Deep Concatenated Residual Networks for Improving Quality of Halftoning-Based BTC Decoded Image
J. Imaging 2021, 7(2), 13; https://0-doi-org.brum.beds.ac.uk/10.3390/jimaging7020013 - 25 Jan 2021
Viewed by 396
Abstract
This paper presents a simple technique for improving the quality of the halftoning-based block truncation coding (H-BTC) decoded image. The H-BTC is an image compression technique inspired from typical block truncation coding (BTC). The H-BTC yields a better decoded image compared to that [...] Read more.
This paper presents a simple technique for improving the quality of the halftoning-based block truncation coding (H-BTC) decoded image. The H-BTC is an image compression technique inspired from typical block truncation coding (BTC). The H-BTC yields a better decoded image compared to that of the classical BTC scheme under human visual observation. However, the impulsive noise commonly appears on the H-BTC decoded image. It induces an unpleasant feeling while one observes this decoded image. Thus, the proposed method presented in this paper aims to suppress the occurring impulsive noise by exploiting a deep learning approach. This process can be regarded as an ill-posed inverse imaging problem, in which the solution candidates of a given problem can be extremely huge and undetermined. The proposed method utilizes the convolutional neural networks (CNN) and residual learning frameworks to solve the aforementioned problem. These frameworks effectively reduce the impulsive noise occurrence, and at the same time, it improves the quality of H-BTC decoded images. The experimental results show the effectiveness of the proposed method in terms of subjective and objective measurements. Full article
(This article belongs to the Special Issue New and Specialized Methods of Image Compression)
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Review

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Review
Performance Overview of the Latest Video Coding Proposals: HEVC, JEM and VVC
J. Imaging 2021, 7(2), 39; https://0-doi-org.brum.beds.ac.uk/10.3390/jimaging7020039 - 22 Feb 2021
Cited by 1 | Viewed by 503
Abstract
The audiovisual entertainment industry has entered a race to find the video encoder offering the best Rate/Distortion (R/D) performance for high-quality high-definition video content. The challenge consists in providing a moderate to low computational/hardware complexity encoder able to run Ultra High-Definition (UHD) video [...] Read more.
The audiovisual entertainment industry has entered a race to find the video encoder offering the best Rate/Distortion (R/D) performance for high-quality high-definition video content. The challenge consists in providing a moderate to low computational/hardware complexity encoder able to run Ultra High-Definition (UHD) video formats of different flavours (360°, AR/VR, etc.) with state-of-the-art R/D performance results. It is necessary to evaluate not only R/D performance, a highly important feature, but also the complexity of future video encoders. New coding tools offering a small increase in R/D performance at the cost of greater complexity are being advanced with caution. We performed a detailed analysis of two evolutions of High Efficiency Video Coding (HEVC) video standards, Joint Exploration Model (JEM) and Versatile Video Coding (VVC), in terms of both R/D performance and complexity. The results show how VVC, which represents the new direction of future standards, has, for the time being, sacrificed R/D performance in order to significantly reduce overall coding/decoding complexity. Full article
(This article belongs to the Special Issue New and Specialized Methods of Image Compression)
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