Abstract
High dynamic range (HDR) image and video technology can provide significant picture quality improvement over the standard dynamic range (SDR). However, when HDR content is represented on an SDR display, dynamic range compression may result in image quality deterioration. To address this problem, we propose an optimized human visual system (HVS) response model-based tone-mapping algorithm to preserve the perceptual responses between the HDR image and its tone-mapped image. First, we measure the HVS response differences using a 2D histogram when an HDR image is displayed on an HDR device and when its tone-mapped image is displayed on an SDR device. Then, we formulate an optimization problem to minimize the differences. By efficiently solving the optimization problem, we obtain an optimal tone-mapping curve. Experimental results on actual displays demonstrate that the proposed algorithm provides superior image quality compared with conventional algorithms in terms of both subjective and objective evaluations.
Highlights
The ultra-high-definition (UHD) television standard, an advanced form of the high-definition television standard, can provide users with improved visual quality by offering increased resolution
To address the aforementioned limitations, we develop an efficient human visual system (HVS) response model-based tone-mapping algorithm that preserves the perceptual quality of tone-mapped images in this work
Our objective is to develop a tone-mapping algorithm that preserves the perceptual responses between the input high dynamic range (HDR) image in HDR10 format and its tone-mapped image presented on an standard dynamic range (SDR) display
Summary
The ultra-high-definition (UHD) television standard, an advanced form of the high-definition television standard, can provide users with improved visual quality by offering increased resolution. UHD broadcasting produces video content with higher quality by improving the resolution, bit depth, and color gamut [1]. Due to the higher dynamic range capability, HDR content can present a look similar to that experienced by the human eye through the human visual system (HVS) [2]. We provide the background, on which the proposed algorithm is based, including 2D histogram equalization, the HDR10 standards, the HVS response model, and relative contrast measurement. We employ 2D histogram equalization [28]–[30] to consider the local details in an HDR image.
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