NoR-VDPNet plus plus : Real-Time No-Reference Image Quality Metrics

Efficiency and efficacy are desirable properties for any evaluation metric having to do with Standard Dynamic Range (SDR) imaging or with High Dynamic Range (HDR) imaging. However, it is a daunting task to satisfy both properties simultaneously. On the one side, existing evaluation metrics like HDR-VDP 2.2 can accurately mimic the Human Visual System (HVS), but this typically comes at a very high computational cost. On the other side, computationally cheaper alternatives (e.g., PSNR, MSE, etc.) fail to capture many crucial aspects of the HVS. In this work, we present NoR-VDPNet++, a deep learning architecture for converting full-reference accurate metrics into no-reference metrics thus reducing the computational burden. We show NoR-VDPNet++ can be successfully employed in different application scenarios.

Automated summary

Efficiency and efficacy are desirable properties for evaluation metrics in SDR and HDR imaging, but it is challenging to satisfy both simultaneously. Existing metrics like HDR-VDP 2.2 accurately mimic the HVS but are computationally expensive, while cheaper alternatives fail to capture crucial aspects of the HVS. In this work, we propose NoR-VDPNet++, a deep learning architecture that converts full-reference metrics to no-reference metrics, reducing computation burden and successfully applied in different scenarios.