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%2C such as PSNR and SSIM%2C 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%2C 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%2C restricting their direct application to HDR content. On the other hand%2C training HDR models from scratch is challenging due to limited available HDR data. In this work%2C 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%2C demonstrating that models trained with our combined recipe outperform previous baselines%2C converge much quicker%2C and reliably generalize to HDR inputs.?quality=80&w=800)
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