HDRTVDN

Reported on 6 benchmarks across 2 tasks · 1 paper · 2 SOTA

Note: results are matched by exact model name. Different papers may use the same name for different model variants.

Computer Vision6 results

inverse tone mappingonMSU HDR Video Reconstruction Benchmark
HDR-SSIM· 2023-03-23
0.9927
SOTA
Learning a Practical SDR-to-HDRTV Up-conversion using New Dataset and Degradation Models arXiv:2303.13031
Inverse-Tone-MappingonMSU HDR Video Reconstruction Benchmark
HDR-SSIM· 2023-03-23
0.9927
SOTA
Learning a Practical SDR-to-HDRTV Up-conversion using New Dataset and Degradation Models arXiv:2303.13031
inverse tone mappingonMSU HDR Video Reconstruction Benchmark
HDR-PSNR· 2023-03-23
35.7459
best: 35.9721 (HDRTVNet)
Learning a Practical SDR-to-HDRTV Up-conversion using New Dataset and Degradation Models arXiv:2303.13031
inverse tone mappingonMSU HDR Video Reconstruction Benchmark
HDR-VQM· 2023-03-23
0.1138
best: 0.263 (SingleHDR)
Learning a Practical SDR-to-HDRTV Up-conversion using New Dataset and Degradation Models arXiv:2303.13031
Inverse-Tone-MappingonMSU HDR Video Reconstruction Benchmark
HDR-PSNR· 2023-03-23
35.7459
best: 35.9721 (HDRTVNet)
Learning a Practical SDR-to-HDRTV Up-conversion using New Dataset and Degradation Models arXiv:2303.13031
Inverse-Tone-MappingonMSU HDR Video Reconstruction Benchmark
HDR-VQM· 2023-03-23
0.1138
best: 0.263 (SingleHDR)
Learning a Practical SDR-to-HDRTV Up-conversion using New Dataset and Degradation Models arXiv:2303.13031