NoR-VDPNet++: real-time no-reference image quality metrics

Banterle F., Artusi A., Moreo A., Carrara F., Cignoni P.

Measurement  General Computer Science  Distortion  General Materials Science  General Engineering  Deep learning  Objective metrics  No-reference  Electrical and Electronic Engineering  HDR imaging  Imaging  Computer architecture  Real-time systems  Convolutional neural networks 

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.

Source: IEEE access 11 (2023): 34544–34553. doi:10.1109/ACCESS.2023.3263496

Publisher: Institute of Electrical and Electronics Engineers, Piscataway, NJ, Stati Uniti d'America

Metrics