NTIRE 2024 Challenge on Blind Enhancement of Compressed Image: Methods and Results
Ren Yang, Radu Timofte, Bingchen Li, Xin Li, Mengxi Guo, Shijie Zhao, Zhang Li, Zhibo Chen, Dongyang Zhang, Yash Kumar Arora, Aditya Arora, Yuanbin Chen, Hui Nee Tang, Tao Wang, Longxuan Zhao, Bin Chen, Tong Tong, Qiao Mo, Jingwei Bao, Jinhua Hao · 2024
This paper reviews the Challenge on Blind Enhancement of Compressed Image at NTIRE 2024, which aims at enhancing the quality of JPEG images which are compressed with unknown quality factor. The challenge requires that the total size of codes and pre-trained model(s) cannot exceed 300 MB, since we encourage solutions for blind enhancement with generalized models, instead of separately training several models for each quality factor. In this report, we summarize the detailed settings of the challenge, the final results, and the solutions proposed by the participants. The challenge has 129 registered participants and received 13 valid submissions. Several teams (including all TOP 3 teams) have publicly released the codes (see Sec. 4). They gauge the state-of-the-art of blind quality enhancement of compressed image.