Quality Assessment of an HDR Dual-Layer Backward-Compatible Codec Compared to Uncompromised SDR and HDR Solutions

Perrin, Anne-Flore; Řeřábek, Martin; Husak, Walt; Ebrahimi, Touradj

doi:10.1109/tbc.2018.2823913

Cited by 3 publications

(1 citation statement)

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“…The associate editor coordinating the review of this manuscript and approving it for publication was Shiqi Wang. inverse tone mapping [2], HDR super-resolution [3], quality assessment [4] and HDR coding [5] rely on HDR values, and display technology is able to reproduce these values [6]. However, the range of most HDR displays is still substantially lower than the values encoded in the HDR content.…”

Section: Introductionmentioning

confidence: 99%

Learning Preferential Perceptual Exposure for HDR Displays

et al. 2019

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High dynamic range (HDR) displays are capable of displaying a wider dynamic range of values than conventional displays. As HDR content becomes more ubiquitous, the use of these displays is likely to accelerate. As HDR displays can present a wider range of values, traditional strategies for mapping HDR content to low dynamic range (LDR) displays can be replaced with either directly displaying values, or using a simple shift mapping (exposure adjustment). The latter approach is especially important when considering ambient lighting, as content viewed in a dark environment may appear substantially different to a bright one. This paper seeks to identify an exposure value which is suitable for displaying specific HDR content on an HDR display under a range of ambient lighting levels. Based on data captured with human participants, this paper establishes user preferred exposure values for a variety of maximum display brightnesses, content and ambient lighting levels. These are then used to develop two models to predict preferred exposure. The first is based on linear regression using straightforward image statistics which require minimal computation and memory to be computed, making this method suitable to be directly used in display hardware. The second is a model based on convolutional neural networks (CNN) to learn image features which best predict exposure values. The CNN model generates better results than the first model at the cost of memory and computation time.INDEX TERMS High dynamic range, machine learning, perception.

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Section: Introductionmentioning

confidence: 99%

Learning Preferential Perceptual Exposure for HDR Displays

et al. 2019

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Removing Banding Artifacts in HDR Videos Generated From Inverse Tone Mapping

Zhou,

Zheng,

Qiu

2024

IEEE Trans. on Broadcast.

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HDR Image Compression by Multi-Scale down Sampling of Intensity Levels

Swamy

Shylashree

2021

Int. J. Image Grap.

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HDR images are inherently very large in size compared to normal images. Hence, storage and communication overheads of HDR images are expensive to be used in mobile devices. Hence, invariably image compression is adopted for HDR images. In this paper, HDR image compression is achieved by down sampling the intensity levels while maintaining the dynamic range same as that of the original. This aspect retains the edge information of the images almost intact. Spatial down-sampling process is used to reduce the number of intensity samples. Consequently, this operation lowers the bit depth required to store the corresponding index file which in turn results in image compression.

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Quality Assessment of an HDR Dual-Layer Backward-Compatible Codec Compared to Uncompromised SDR and HDR Solutions

Cited by 3 publications

References 15 publications

Learning Preferential Perceptual Exposure for HDR Displays

Learning Preferential Perceptual Exposure for HDR Displays

Removing Banding Artifacts in HDR Videos Generated From Inverse Tone Mapping

HDR Image Compression by Multi-Scale down Sampling of Intensity Levels

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