JVET-AC0052 EE1-2.4: CNN filter Based on RPR-based SR Combined with GOP Level Adaptive Resolution [S. Huang, C. Jung (Xidian Univ.), Y. Liu, M. Li (OPPO), J. Nam, S. Yoo, J. Lim, S. H. Kim (LGE)]
This contribution reports the EE1-2.4 test results, which is a combination of JVET-AB0093 and JVET-Z0065 test 2.1.1. At each GOP level, the encoder can adaptively select a scale factor from 1.0x and 2.0x and CNN-based super-resolution is utilized for is the latter case. Compared with VTM-11.0-nnvc-2.0, the test 2.4.1 experimental results show {-4.14%(Y), -0.33%(U), -0.22%(V)} and {-3.73%(Y), -3.07%(U), -1.15%(V)} and the test 2.4.2 experimental results show {-4.98%(Y), 0.07%(U), -0.78%(V)} and {-3.73%(Y), -3.07%(U), -1.15%(V)} BD-rate gains on average (A1 and A2 classes), under AI and RA configurations.
As a general comment, the constraint of 10% rate matching and showing PSNR graphs allows much better interpretation of SR results.
It was also commented that it would be beneficial to provide SSIM resuls in SR proposals.