131 research outputs found

    Algorithms for compression of high dynamic range images and video

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    The recent advances in sensor and display technologies have brought upon the High Dynamic Range (HDR) imaging capability. The modern multiple exposure HDR sensors can achieve the dynamic range of 100-120 dB and LED and OLED display devices have contrast ratios of 10^5:1 to 10^6:1. Despite the above advances in technology the image/video compression algorithms and associated hardware are yet based on Standard Dynamic Range (SDR) technology, i.e. they operate within an effective dynamic range of up to 70 dB for 8 bit gamma corrected images. Further the existing infrastructure for content distribution is also designed for SDR, which creates interoperability problems with true HDR capture and display equipment. The current solutions for the above problem include tone mapping the HDR content to fit SDR. However this approach leads to image quality associated problems, when strong dynamic range compression is applied. Even though some HDR-only solutions have been proposed in literature, they are not interoperable with current SDR infrastructure and are thus typically used in closed systems. Given the above observations a research gap was identified in the need for efficient algorithms for the compression of still images and video, which are capable of storing full dynamic range and colour gamut of HDR images and at the same time backward compatible with existing SDR infrastructure. To improve the usability of SDR content it is vital that any such algorithms should accommodate different tone mapping operators, including those that are spatially non-uniform. In the course of the research presented in this thesis a novel two layer CODEC architecture is introduced for both HDR image and video coding. Further a universal and computationally efficient approximation of the tone mapping operator is developed and presented. It is shown that the use of perceptually uniform colourspaces for internal representation of pixel data enables improved compression efficiency of the algorithms. Further proposed novel approaches to the compression of metadata for the tone mapping operator is shown to improve compression performance for low bitrate video content. Multiple compression algorithms are designed, implemented and compared and quality-complexity trade-offs are identified. Finally practical aspects of implementing the developed algorithms are explored by automating the design space exploration flow and integrating the high level systems design framework with domain specific tools for synthesis and simulation of multiprocessor systems. The directions for further work are also presented

    High Dynamic Range Images Coding: Embedded and Multiple Description

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    The aim of this work is to highlight and discuss a new paradigm for representing high-dynamic range (HDR) images that can be used for both its coding and describing its multimedia content. In particular, the new approach defines a new representation domain that, conversely from the classical compressed one, enables to identify and exploit content metadata. Information related to content are used here to control both the encoding and the decoding process and are directly embedded in the compressed data stream. Firstly, thanks to the proposed solution, the content description can be quickly accessed without the need of fully decoding the compressed stream. This fact ensures a significant improvement in the performance of search and retrieval systems, such as for semantic browsing of image databases. Then, other potential benefits can be envisaged especially in the field of management and distribution of multimedia content, because the direct embedding of content metadata preserves the consistency between content stream and content description without the need of other external frameworks, such as MPEG-21. The paradigm proposed here may also be shifted to Multiple description coding, where different representations of the HDR image can be generated accordingly to its content. The advantages provided by the new proposed method are visible at different levels, i.e. when evaluating the redundancy reduction. Moreover, the descriptors extracted from the compressed data stream could be actively used in complex applications, such as fast retrieval of similar images from huge databases

    Color space selection for JPEG image compression

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    The Joint Photographic Experts Group\u27s image compression algorithm has been shown to be a very efficient and powerful method of compressing images. However, there is little substantive information about which color space should be utilized when implementing the JPEG algorithm. Currently, the JPEG algorithm is set up for use with any three component color space. The objective of this research was to determine whether or not the color space selected will significantly improve image compression capabilities. The RGB, XYZ, YIQ, CIELAB, CIELUV, and CIELAB LCh color spaces were examined and compared. Both numerical measures and psychophysical techniques were used to assess the results. The final results indicate that the device space, RGB, is the worst color space to compress images. In comparison, the nonlinear transforms of the device space, CIELAB and CIELUV, are the best color spaces to compress images. The XYZ, YIQ, and CIELAB LCh color spaces resulted in intermediate levels of compression

    JNCD-based perceptual compression of RGB 4:4:4 image data

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    In contemporary lossy image coding applications, a desired aim is to decrease, as much as possible, bits per pixel without inducing perceptually conspicuous distortions in RGB image data. In this paper, we propose a novel color-based perceptual compression technique, named RGB-PAQ. RGB-PAQ is based on CIELAB Just Noticeable Color Difference (JNCD) and Human Visual System (HVS) spectral sensitivity. We utilize CIELAB JNCD and HVS spectral sensitivity modeling to separately adjust quantization levels at the Coding Block (CB) level. In essence, our method is designed to capitalize on the inability of the HVS to perceptually differentiate photons in very similar wavelength bands. In terms of application, the proposed technique can be used with RGB (4:4:4) image data of various bit depths and spatial resolutions including, for example, true color and deep color images in HD and Ultra HD resolutions. In the evaluations, we compare RGB-PAQ with a set of anchor methods; namely, HEVC, JPEG, JPEG 2000 and Google WebP. Compared with HEVC HM RExt, RGB-PAQ achieves up to 77.8% bits reductions. The subjective evaluations confirm that the compression artifacts induced by RGB-PAQ proved to be either imperceptible (MOS = 5) or near-imperceptible (MOS = 4) in the vast majority of cases

    Exclusive-or preprocessing and dictionary coding of continuous-tone images.

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    The field of lossless image compression studies the various ways to represent image data in the most compact and efficient manner possible that also allows the image to be reproduced without any loss. One of the most efficient strategies used in lossless compression is to introduce entropy reduction through decorrelation. This study focuses on using the exclusive-or logic operator in a decorrelation filter as the preprocessing phase of lossless image compression of continuous-tone images. The exclusive-or logic operator is simply and reversibly applied to continuous-tone images for the purpose of extracting differences between neighboring pixels. Implementation of the exclusive-or operator also does not introduce data expansion. Traditional as well as innovative prediction methods are included for the creation of inputs for the exclusive-or logic based decorrelation filter. The results of the filter are then encoded by a variation of the Lempel-Ziv-Welch dictionary coder. Dictionary coding is selected for the coding phase of the algorithm because it does not require the storage of code tables or probabilities and because it is lower in complexity than other popular options such as Huffman or Arithmetic coding. The first modification of the Lempel-Ziv-Welch dictionary coder is that image data can be read in a sequence that is linear, 2-dimensional, or an adaptive combination of both. The second modification of the dictionary coder is that the coder can instead include multiple, dynamically chosen dictionaries. Experiments indicate that the exclusive-or operator based decorrelation filter when combined with a modified Lempel-Ziv-Welch dictionary coder provides compression comparable to algorithms that represent the current standard in lossless compression. The proposed algorithm provides compression performance that is below the Context-Based, Adaptive, Lossless Image Compression (CALIC) algorithm by 23%, below the Low Complexity Lossless Compression for Images (LOCO-I) algorithm by 19%, and below the Portable Network Graphics implementation of the Deflate algorithm by 7%, but above the Zip implementation of the Deflate algorithm by 24%. The proposed algorithm uses the exclusive-or operator in the modeling phase and uses modified Lempel-Ziv-Welch dictionary coding in the coding phase to form a low complexity, reversible, and dynamic method of lossless image compression

    JPEG XT: A Compression Standard for HDR and WCG Images [Standards in a Nutshell]

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    High bit depth data acquisition and manipulation have been largely studied at the academic level over the last 15 years and are rapidly attracting interest at the industrial level. An example of the increasing interest for high-dynamic range (HDR) imaging is the use of 32-bit floating point data for video and image acquisition and manipulation that allows a variety of visual effects that closely mimic the real-world visual experience of the end user [1] (see Figure 1). At the industrial level, we are witnessing increasing traction toward supporting HDR and wide color gamut (WCG). WCG leverages HDR for each color channel to display a wider range of colors. Consumer cameras are currently available with a 14- or 16-bit analog-to-digital converter. Rendering devices are also appearing with the capability to display HDR images and video with a peak brightness of up to 4,000 nits and to support WCG (ITU-R Rec. BT.2020 [2]) rather than the historical ITU-R Rec. BT.709 [3]. This trend calls for a widely accepted standard for higher bit depth support that can be seamlessly integrated into existing products and applications. While standard formats such as the Joint Photographic Experts Group (JPEG) 2000 [5] and JPEG XR [6] offer support for high bit depth image representations, their adoption requires a nonnegligible investment that may not always be affordable in existing imaging ecosystems, and induces a difficult transition, as they are not backward-compatible with the popular JPEG image format

    Encoding high dynamic range and wide color gamut imagery

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    In dieser Dissertation wird ein szenischer Bewegtbilddatensatz mit erweitertem Dynamikumfang (High Dynamic Range, HDR) und großem Farbumfang (Wide Color Gamut, WCG) eingefĂŒhrt und es werden Modelle zur Kodierung von HDR und WCG Bildern vorgestellt. Die objektive und visuelle Evaluation neuer HDR und WCG Bildverarbeitungsalgorithmen, Kompressionsverfahren und BildwiedergabegerĂ€te erfordert einen Referenzdatensatz hoher QualitĂ€t. Daher wird ein neuer HDR- und WCG-Video-Datensatz mit einem Dynamikumfang von bis zu 18 fotografischen Blenden eingefĂŒhrt. Er enthĂ€lt inszenierte und dokumentarische Szenen. Die einzelnen Szenen sind konzipiert um eine Herausforderung fĂŒr Tone Mapping Operatoren, Gamut Mapping Algorithmen, Kompressionscodecs und HDR und WCG BildanzeigegerĂ€te darzustellen. Die Szenen sind mit professionellem Licht, Maske und Filmausstattung aufgenommen. Um einen cinematischen Bildeindruck zu erhalten, werden digitale Filmkameras mit ‘Super-35 mm’ SensorgrĂ¶ĂŸe verwendet. Der zusĂ€tzliche Informationsgehalt von HDR- und WCG-Videosignalen erfordert im Vergleich zu Signalen mit herkömmlichem Dynamikumfang eine neue und effizientere Signalkodierung. Ein Farbraum fĂŒr HDR und WCG Video sollte nicht nur effizient quantisieren, sondern wegen der unterschiedlichen Monitoreigenschaften auf der EmpfĂ€ngerseite auch fĂŒr die Dynamik- und Farbumfangsanpassung geeignet sein. Bisher wurden Methoden fĂŒr die Quantisierung von HDR Luminanzsignalen vorgeschlagen. Es fehlt jedoch noch ein entsprechendes Modell fĂŒr Farbdifferenzsignale. Es werden daher zwei neue FarbrĂ€ume eingefĂŒhrt, die sich sowohl fĂŒr die effiziente Kodierung von HDR und WCG Signalen als auch fĂŒr die Dynamik- und Farbumfangsanpassung eignen. Diese FarbrĂ€ume werden mit existierenden HDR und WCG Farbsignalkodierungen des aktuellen Stands der Technik verglichen. Die vorgestellten Kodierungsschemata erlauben es, HDR- und WCG-Video mittels drei FarbkanĂ€len mit 12 Bits tonaler Auflösung zu quantisieren, ohne dass Quantisierungsartefakte sichtbar werden. WĂ€hrend die Speicherung und Übertragung von HDR und WCG Video mit 12-Bit Farbtiefe pro Kanal angestrebt wird, unterstĂŒtzen aktuell verbreitete Dateiformate, Videoschnittstellen und Kompressionscodecs oft nur niedrigere Bittiefen. Um diese existierende Infrastruktur fĂŒr die HDR VideoĂŒbertragung und -speicherung nutzen zu können, wird ein neues bildinhaltsabhĂ€ngiges Quantisierungsschema eingefĂŒhrt. Diese Quantisierungsmethode nutzt Bildeigenschaften wie Rauschen und Textur um die benötigte tonale Auflösung fĂŒr die visuell verlustlose Quantisierung zu schĂ€tzen. Die vorgestellte Methode erlaubt es HDR Video mit einer Bittiefe von 10 Bits ohne sichtbare Unterschiede zum Original zu quantisieren und kommt mit weniger Rechenkraft im Vergleich zu aktuellen HDR Bilddifferenzmetriken aus
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