With the development of imaging and display technologies, ultra-high definition video, high dynamic range (HDR) video, immersive 360-degree video, etc., have emerged in our lives. However, with the increased resolution and imaging technologies, the data volume of these videos increases dramatically. The huge video data becomes a challenge for storage and transmission. In order to address these challenges, it is desirable to design efficient video compression algorithms. Moreover, since there is a quality loss during the video data compression and transmission processes, it is important to design visual quality assessment algorithms to distinguish the poor-quality video frames. Nowadays, artificial intelligence (AI) is widely used in academia and industry. Deep learning is regarded as one of the important AI technologies that has been successfully applied in areas such as image processing, computer vision, and pattern recognition. Currently, the traditional video compression and visual quality assessment methods face a lot of challenges, including high computational complexity, limited coding efficiency, and low prediction accuracy. Deep learning provides a new way to solve these problems.
This special issue is intended for researchers and practitioners from academia as well as industry who are interested in issues that arise from using deep learning for video data compression and visual quality assessment.
The topics of interest include, but are not limited to:
- Deep learning for image/video compression
- Deep learning for intra prediction coding
- Deep learning for inter prediction coding
- Deep learning for rate control and bit allocation optimizations
- Deep learning for filtering algorithms
- Deep learning for image/video quality enhancement
- Deep learning for low-complexity video coding algorithms
- Deep learning for coding efficiency optimization
- Deep learning for Versatile Video Coding (VVC) optimization
- Deep learning for 3D/HDR/360-degree video coding
- Deep learning for image/video quality assessment
Dr. Zhaoqing Pan (Lead Guest Editor)Nanjing University of Information Science and Technology, ChinaEmail: [email protected]
Dr. Byeungwoo JeonSungkyunkwan University, South KoreaEmail: [email protected]
Dr. Nam LingSanta Clara University, USAEmail: [email protected]
Dr. Bo PengTianjin University, ChinaEmail: [email protected]
Submission deadline: 30 June, 2021
Authors should prepare their manuscript according to the Instructions for Authors available from the Multimedia Tools and Applications website. Authors should submit through the online submission site at https://www.editorialmanager.com/mtap/default.aspx and select “SI 1221 – Deep Learning for Image/Video Compression and Visual Quality Assessment” when they reach the “Article Type” step in the submission process. Submitted papers should present original, unpublished work, relevant to one of the topics of the special issue. All submitted papers will be evaluated on the basis of relevance, significance of contribution, technical quality, scholarship, and quality of presentation, by at least three independent reviewers. It is the policy of the journal that no submission, or substantially overlapping submission, be published or be under review at another journal or conference at any time during the review process.
The special issue will consider papers extending previously published conference papers, provided the journal submission presents a significant contribution beyond the conference paper. Authors must explain in the introduction to the paper the new contribution to the field made by the submission, and the original conference publication should be cited in the text. Note that neither verbatim transfer of large parts of the conference paper nor wholesale reproduction of already published figures is acceptable.