Multimedia Tools and Applications - Call for Papers: Robust Enhancement, Understanding and Assessment of Low-quality Multimedia Data [1233]
Low-quality multimedia data (including low resolution, low illumination, defects, blurriness, etc.) often pose a challenge for content understanding, as many visual task algorithms are developed on clear image or video data under high resolution and good visibility. Taking the light condition as an example, early research focused on high-quality images or daytime scenes with better illumination and existing vision techniques have achieved improved results with an approximate accuracy of 96% with these conditions. Nevertheless, in practice, over 90% of criminal activities occur in low-quality nighttime scenes, in which the image/video data collected by the surveillance system has low contrast and poor quality.
To alleviate this problem, data enhancement techniques (super-resolution, low-light enhancement, derain, and inpainting) have been developed to restore low-quality multimedia data. Efforts are also being made to develop robust content understanding algorithms in adverse weather and lighting conditions. Some quality assessment techniques aiming at evaluating the analytical quality of data have also emerged. Even though these topics are mostly studied independently, they are tightly related in terms of ensuring a robust understanding of multimedia content. Therefore, this special issue will inspire readers from both academia and industry, and facilitate research in computer vision and multimedia for a robust understanding of low-quality data in the broader context of multimedia applications. The aim of this special issue is to: 1) bring together leading experts from academia and industry to discuss the current state of the art, challenges, and future steps in low-quality multimedia data understanding; 2) call for a coordinated effort to understand the opportunities and challenges emerging in low-quality multimedia data understanding; 3) identify key tasks and evaluate the state-of-the-art-methods; 4) present innovative methodologies and ideas; 5) propose new real-world low-quality multimedia datasets and discuss future directions. To this purpose, we seek original research and survey papers on the following areas, but not limited to:
- Emerging trends in data enhancement, analysis, or evaluation
- Enhancement for a robust content understanding of low-quality data
- Image/Video Quality Assessment for a robust content understanding of low-quality data
- Assessment-guided visual content enhancement of low-quality data
- Joint embedding learning for enhancement, analysis, and evaluation
- Explainable image/video enhancement methods
- Degradation-aware image/video quality assessment methods
- Real-world low-quality multimedia analysis datasets
This special issue focuses on combining low-level enhancement and evaluation tasks with high-level analysis tasks and breaking through the interference, as result in improving the performance of the high-level multimedia content understanding.
Guest Editors:
Liang Liao (Lead GE), Research Fellow, Nanyang Technological University, liang.liao@ntu.edu.sg
Qiong Liu, Professor, Huazhong University of Science and Technology, q.liu@hust.edu.cn
Chih-Chung Hsu, Assistant Professor, National Cheng Kung University, cchsu@gs.ncku.edu.tw
Xian Zhong, Associated Professor, Wuhan University of Technology, zhongx@whut.edu.cn
Xiao Wang, Associated Professor, Wuhan University of Science and Technology, wangxiao2021@wust.edu.cn
Kui Jiang, Research Scientist, Cloud BU, Huawei, jiangjui5@huaweu.com
Important Dates:
Submission deadline: 20 June 2023
Reviewing deadline: 20 July 2023
Author revision deadline: 14 September 2023
Final notification date: 19 October 2023
Submission Guidelines:
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 1233 - Robust Enhancement, Understanding and Assessment of Low-quality Multimedia Data” 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. Please note that the authors of selected papers presented at UoLMM 2022 are invited to submit an extended version of their contributions by taking into consideration both the reviewers’ comments on their conference paper, and the feedback received during presentation at the conference. It is worth clarifying that the extended version is expected to contain a substantial scientific contribution, e.g., in the form of new algorithms, experiments or qualitative/quantitative comparisons, and that neither verbatim transfer of large parts of the conference paper nor reproduction of already published figures will be tolerated. The extended versions of UoLMM papers will undergo the standard, rigorous journal review process and be accepted only if well-suited to the topic of this special issue and meeting the scientific level of the journal. Final decisions on all papers are made by the Editor in Chief.