Adapting Pretrained Networks for Image Quality Assessment on High
  Dynamic Range Displays
Conventional image quality metrics (IQMs), such as PSNR and SSIM, are
designed for perceptually uniform gamma-encoded pixel values and cannot be
directly applied to perceptually non-uniform linear high-dynamic-range (HDR)
colors. Similarly, most of the available datasets consist of
standard-dynamic-range (SDR) images collected in standard and possibly
uncontrolled viewing conditions. Popular pre-trained neural networks are
likewise intended for SDR inputs, restricting their direct application to HDR
content. On the other hand, training HDR models from scratch is challenging due
to limited available HDR data. In this work, we explore more effective
approaches for training deep learning-based models for image quality assessment
(IQA) on HDR data. We leverage networks pre-trained on SDR data (source domain)
and re-target these models to HDR (target domain) with additional fine-tuning
and domain adaptation. We validate our methods on the available HDR IQA
datasets, demonstrating that models trained with our combined recipe outperform
previous baselines, converge much quicker, and reliably generalize to HDR
inputs.
Andrei Chubarau Hyunjin Yoo Tara Akhavan James Clark