The color gamut supported by current commercial displays is only a subset of the full spectrum of colors visible by the human eye. In High-Definition (HD) television technology, the scope of the supported colors covers 35.9% of the full visible gamut. For comparison, Ultra High-Definition (UHD) television, which is currently being deployed on the market, extends this range to 75.8%. However, when reproducing content with a wider color gamut than that of a television, typically UHD content on HD television, some original color information may lie outside the reproduction capabilities of the television. Efficient gamut mapping techniques are required in order to fit the colors of any source content into the gamut of a given display. The goal of gamut mapping is to minimize the distortion, in terms of perceptual quality, when converting video from one color gamut to another. It is assumed that the efficiency of gamut mapping depends on the color space in which it is computed. In this article, we evaluate 14 gamut mapping techniques, 12 combinations of two projection methods across six color spaces as well as R’G’B’ Clipping and wrong gamut interpretation. Objective results, using the CIEDE2000 metric, show that the R’G’B’ Clipping is slightly outperformed by only one combination of color space and projection method. However, analysis of images shows that R’G’B’ Clipping can result in loss of contrast in highly saturated images, greatly impairing the quality of the mapped image.
KEYWORDS: Computer programming, High dynamic range imaging, Quantization, Phase transfer function, Video compression, Image processing, Video, Visualization, Distortion, Human vision and color perception
Traditional Low Dynamic Range (LDR) color spaces encode a small fraction of the visible color gamut, which does not encompass the range of colors produced on upcoming High Dynamic Range (HDR) displays. Future imaging systems will require encoding much wider color gamut and luminance range. Such wide color gamut can be represented using floating point HDR pixel values but those are inefficient to encode. They also lack perceptual uniformity of the luminance and color distribution, which is provided (in approximation) by most LDR color spaces. Therefore, there is a need to devise an efficient, perceptually uniform and integer valued representation for high dynamic range pixel values. In this paper we evaluate several methods for encoding colour HDR pixel values, in particular for use in image and video compression. Unlike other studies we test both luminance and color difference encoding in a rigorous 4AFC threshold experiments to determine the minimum bit-depth required. Results show that the Perceptual Quantizer (PQ) encoding provides the best perceptual uniformity in the considered luminance range, however the gain in bit-depth is rather modest. More significant difference can be observed between color difference encoding schemes, from which YDuDv encoding seems to be the most efficient.
KEYWORDS: High dynamic range imaging, Quantization, Video, Video compression, Computer programming, Time multiplexed optical shutter, Video coding, RGB color model, Visualization, Distortion
Tone Mapping Operators (TMOs) compress High Dynamic Range (HDR) content to address Low Dynamic Range (LDR) displays. However, before reaching the end-user, this tone mapped content is usually compressed for broadcasting or storage purposes. Any TMO includes a quantization step to convert floating point values to integer ones. In this work, we propose to adapt this quantization, in the loop of an encoder, to reduce the entropy of the tone mapped video content. Our technique provides an appropriate quantization for each mode of both the Intra and Inter-prediction that is performed in the loop of a block-based encoder. The mode that minimizes a rate-distortion criterion uses its associated quantization to provide integer values for the rest of the encoding process. The method has been implemented in HEVC and was tested over two different scenarios: the compression of tone mapped LDR video content (using the HM10.0) and the compression of perceptually encoded HDR content (HM14.0). Results show an average bit-rate reduction under the same PSNR for all the sequences and TMO considered of 20.3% and 27.3% for tone mapped content and 2.4% and 2.7% for HDR content.
KEYWORDS: Video, High dynamic range imaging, Time multiplexed optical shutter, Video processing, Associative arrays, Visualization, Image processing, Cameras, Sun, RGB color model
Tone Mapping Operators (TMOs) aim at converting real world high dynamic range (HDR) images captured with HDR cameras, into low dynamic range (LDR) images that can be displayed on LDR displays. Several TMOs have been proposed over the last decade, from the simple global mapping to the more complex one simulating the human vision system. While these solutions work generally well for still pictures, they are usually less e_cient for video sequences as they are source of visual artifacts. Only few of them can be adapted to cope with a sequence of images. In this paper we present a major problem that a static TMO usually encounters while dealing with video sequences, namely the temporal coherency. Indeed, as each tone mapper deals with each frame separately, no temporal coherency is taken into account and hence the results can be quite disturbing for high varying dynamics in a video. We propose a temporal coherency algorithm that is designed to analyze a video as a whole, and from its characteristics adapts each tone mapped frame of a sequence in order to preserve the temporal coherency. This temporal coherency algorithm has been tested on a set of real as well as Computer Graphics Image (CGI) content and put in competition with several algorithms that are designed to be time-dependent. Results show that temporal coherency preserves the overall contrast in a sequence of images. Furthermore, this technique is applicable to any TMO as it is a post-processing that only depends on the used TMO.
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