Temporally coherent luminance-to-luma mapping for high dynamic range video coding with H.264/AVC

Garbas, Jens-Uwe; Thoma, Herbert

doi:10.1109/icassp.2011.5946532

Cited by 22 publications

(22 citation statements)

References 9 publications

(21 reference statements)

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“…We will refer to this method as HDRV for the remainder of this paper. Garbas and Thoma [17] presented a temporally coherent extension of the Adaptive LogLUV function [26] suitable for HDR video compression [17]. The proposed method maps real-world luminance into a 12-bit luma space and preserves chrominance in 8-bit u v chroma channels similar to LogLUV [19].…”

Section: Related Workmentioning

confidence: 99%

An evaluation of power transfer functions for HDR video compression

et al. 2016

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High dynamic range (HDR) imaging enables the full range of light in a scene to be captured, transmitted and displayed. However, uncompressed 32-bit HDR is four times larger than traditional low dynamic range (LDR) imagery. If HDR is to fulfil its potential for use in live broadcasts and interactive remote gaming, fast, efficient compression is necessary for HDR video to be manageable on existing communications infrastructure. A number of methods have been put forward for HDR video compression. However, these can be relatively complex and frequently require the use of multiple video streams. In this paper, we propose the use of a straightforward Power Transfer Function (PTF) as a practical, computationally fast, HDR video compression solution. The use of PTF is presented and evaluated against four other HDR video compression methods. An objective evaluation shows that PTF exhibits improved quality at a range of bitrates and, due to its straightforward nature, is highly suited for real-time HDR video applications.

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Section: Related Workmentioning

confidence: 99%

An evaluation of power transfer functions for HDR video compression

et al. 2016

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“…The non-backward compatible approach, shown in Figure 1a, takes advantage of the higher bit-depth support (typically 10-14 bits) in state-of-the-art video encoders [44,43]. Using a range of transfer functions which work on either linear RGB data or perform colorspace conversions followed by luminance to luma mapping [21,29,14] they manipulate captured data in order to pack as much information as possible within the specified bit-depth limit. The resultant optimized video stream is then passed to the encoder.…”

Section: Methodsmentioning

confidence: 99%

“…Garbas and Thoma (2011) [14] proposed a non-backward compatible algorithm by modifying the Adaptive LogLuv algorithm [33] and adding temporal coherence to minimize flickering artefacts in HDR video. The algorithm converts linear RGB values to Lu'v' colorspace by converting real-world luminance to 12-bit luma and adjusting color information into 8-bit u and v channels similar to LogLUV encoding [21].…”

Section: Garbas and Thoma (Fraunhofer)mentioning

confidence: 99%

Objective and subjective evaluation of High Dynamic Range video compression

Mukherjee

Debattista

Bashford-Rogers

et al. 2016

Signal Processing: Image Communication

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Copyright and reuse:The Warwick Research Archive Portal (WRAP) makes this work by researchers of the University of Warwick available open access under the following conditions. Copyright © and all moral rights to the version of the paper presented here belong to the individual author(s) and/or other copyright owners. To the extent reasonable and practicable the material made available in WRAP has been checked for eligibility before being made available.Copies of full items can be used for personal research or study, educational, or not-for-profit purposes without prior permission or charge. Provided that the authors, title and full bibliographic details are credited, a hyperlink and/or URL is given for the original metadata page and the content is not changed in any way. A note on versions:The version presented here may differ from the published version or, version of record, if you wish to cite this item you are advised to consult the publisher's version. Please see the 'permanent WRAP URL' above for details on accessing the published version and note that access may require a subscription. AbstractA number of High Dynamic Range (HDR) video compression algorithms proposed to date have either been developed in isolation or only-partially compared with each other. Previous evaluations were conducted using quality assessment error metrics, which for the most part were developed for qualitative assessment of Low Dynamic Range (LDR) videos. This paper presents a comprehensive objective and subjective evaluation conducted with six published HDR video compression algorithms. The objective evaluation was undertaken on a large set of 39 HDR video sequences using seven numerical error metrics namely: PSNR, logPSNR, puPSNR, puSSIM, Weber MSE, HDR-VDP and HDR-VQM. The subjective evaluation involved six short-listed sequences and two ranking-based subjective experiments with hidden reference at two different output bitrates with 32 participants each, who were tasked to rank distorted HDR video footage compared to an uncompressed version of the same footage. Results suggest a strong correlation between the objective and subjective evaluation. Also, non-backward compatible compression algorithms appear to perform better at lower output bit rates than backward compatible algorithms across the settings used in this evaluation.

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“…LDR is typically stored using just 24 Since Wards Radiance format [7], a number of other ways of representing HDR pixels have been proposed, including LogLuv encoding used in a custom HDR-version of the TIFF images [19], and OpenEXR. Developed by Industrial Light and Magic for the film industry, OpenEXR has since been released as free software [20].…”

Section: Storagementioning

confidence: 99%

“…Examples of one-stream HDR video compression include HDRv [23], adaptive LogLuv [24], PQ [25], HLG [26] and the Power Transfer Function method [27].…”

Section: Compressionmentioning

confidence: 99%

HDR video past, present and future: A perspective

Chalmers

Debattista

2017

Signal Processing: Image Communication

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High Dynamic Range (HDR) video has emerged from research labs around the world and entered the realm of consumer electronics. The dynamic range that a human can see in a scene with minimal eye adaption (approximately 1,000,000 : 1) is vastly greater than traditional imaging technology which can only capture about 8 f-stops (256 : 1). HDR technology, on the other hand, has the potential to capture the full range of light in a scene; even more than a human eye can see. This paper examines the field of HDR video from capture to display; past, present and future. In particular the paper looks beyond the current marketing hype around HDR, to show how HDR video in the future can and, indeed, should bring about a step change in imaging, analogous to the change from black and white to colour.

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Temporally coherent luminance-to-luma mapping for high dynamic range video coding with H.264/AVC

Cited by 22 publications

References 9 publications

An evaluation of power transfer functions for HDR video compression

An evaluation of power transfer functions for HDR video compression

Objective and subjective evaluation of High Dynamic Range video compression

HDR video past, present and future: A perspective

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