1,863 research outputs found

    High dynamic range video compression exploiting luminance masking

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    Towards one video encoder per individual : guided High Efficiency Video Coding

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    Compression of Infrared images

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    High Dynamic Range Visual Content Compression

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    This thesis addresses the research questions of High Dynamic Range (HDR) visual contents compression. The HDR representations are intended to represent the actual physical value of the light rather than exposed value. The current HDR compression schemes are the extension of legacy Low Dynamic Range (LDR) compressions, by using Tone-Mapping Operators (TMO) to reduce the dynamic range of the HDR contents. However, introducing TMO increases the overall computational complexity, and it causes the temporal artifacts. Furthermore, these compression schemes fail to compress non-salient region differently than the salient region, when Human Visual System (HVS) perceives them differently. The main contribution of this thesis is to propose a novel Mapping-free visual saliency-guided HDR content compression scheme. Firstly, the relationship of Discrete Wavelet Transform (DWT) lifting steps and TMO are explored. A novel approach to compress HDR image by Joint Photographic Experts Group (JPEG) 2000 codec while backward compatible to LDR is proposed. This approach exploits the reversibility of tone mapping and scalability of DWT. Secondly, the importance of the TMO in the HDR compression is evaluated in this thesis. A mapping-free post HDR image compression based on JPEG and JPEG2000 standard codecs for current HDR image formats is proposed. This approach exploits the structure of HDR formats. It has an equivalent compression performance and the lowest computational complexity compared to the existing HDR lossy compressions (50% lower than the state-of-the-art). Finally, the shortcomings of the current HDR visual saliency models, and HDR visual saliency-guided compression are explored in this thesis. A spatial saliency model for HDR visual content outperform others by 10% for spatial visual prediction task with 70% lower computational complexity is proposed. Furthermore, the experiment suggested more than 90% temporal saliency is predicted by the proposed spatial model. Moreover, the proposed saliency model can be used to guide the HDR compression by applying different quantization factor according to the intensity of predicted saliency map

    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

    ๋‹ค์ค‘ ๋…ธ์ถœ ์ž…๋ ฅ์˜ ํ”ผ์ณ ๋ถ„ํ•ด๋ฅผ ํ†ตํ•œ ํ•˜์ด ๋‹ค์ด๋‚˜๋ฏน ๋ ˆ์ธ์ง€ ์˜์ƒ ์ƒ์„ฑ ๋ฐฉ๋ฒ•

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ํ˜‘๋™๊ณผ์ • ์ธ๊ณต์ง€๋Šฅ์ „๊ณต, 2022. 8. ์กฐ๋‚จ์ต.Multi-exposure high dynamic range (HDR) imaging aims to generate an HDR image from multiple differently exposed low dynamic range (LDR) images. Multi-exposure HDR imaging is a challenging task due to two major problems. One is misalignments among the input LDR images, which can cause ghosting artifacts on result HDR, and the other is missing information on LDR images due to under-/over-exposed region. Although previous methods tried to align input LDR images with traditional methods(e.g., homography, optical flow), they still suffer undesired artifacts on the result HDR image due to estimation errors that occurred in aligning step. In this dissertation, disentangled feature-guided HDR network (DFGNet) is proposed to alleviate the above-stated problems. Specifically, exposure features and spatial features are first extracted from input LDR images, and they are disentangled from each other. Then, these features are processed through the proposed DFG modules, which produce a high-quality HDR image. The proposed DFGNet shows outstanding performance compared to previous methods, achieving the PSNR-โ„“ of 41.89dB and the PSNR-ฮผ of 44.19dB.๋‹ค์ค‘ ๋…ธ์ถœ(Multiple-exposure) ํ•˜์ด ๋‹ค์ด๋‚˜๋ฏน ๋ ˆ์ธ์ง€(High Dynamic Range, HDR) ์ด๋ฏธ์ง•์€ ๊ฐ๊ฐ ๋‹ค๋ฅธ ๋…ธ์ถœ ์ •๋„๋กœ ์ดฌ์˜๋œ ๋‹ค์ˆ˜์˜ ๋กœ์šฐ ๋‹ค์ด๋‚˜๋ฏน ๋ ˆ์ธ์ง€(Low Dynamic Range, LDR) ์ด๋ฏธ์ง€๋ฅผ ์‚ฌ์šฉํ•˜์—ฌ ํ•˜๋‚˜์˜ HDR ์ด๋ฏธ์ง€๋ฅผ ์ƒ์„ฑํ•˜๋Š” ๊ฒƒ์„ ๋ชฉํ‘œ๋กœ ํ•œ๋‹ค. ๋‹ค์ค‘ ๋…ธ์ถœ HDR ์ด๋ฏธ์ง•์€ ๋‘ ๊ฐ€์ง€ ์ฃผ์š” ๋ฌธ์ œ์  ๋•Œ๋ฌธ์— ์–ด๋ ค์›€์ด ์žˆ๋Š”๋ฐ, ํ•˜๋‚˜๋Š” ์ž…๋ ฅ LDR ์ด๋ฏธ์ง€๋“ค์ด ์ •๋ ฌ๋˜์ง€ ์•Š์•„ ๊ฒฐ๊ณผ HDR ์ด๋ฏธ์ง€์—์„œ ๊ณ ์ŠคํŠธ ์•„ํ‹ฐํŒฉํŠธ(Ghosting Artifact)๊ฐ€ ๋ฐœ์ƒํ•  ์ˆ˜ ์žˆ๋‹ค๋Š” ์ ๊ณผ, ๋˜ ๋‹ค๋ฅธ ํ•˜๋‚˜๋Š” LDR ์ด๋ฏธ์ง€๋“ค์˜ ๊ณผ์†Œ๋…ธ์ถœ(Under-exposure) ๋ฐ ๊ณผ๋‹ค๋…ธ์ถœ(Over-exposure) ๋œ ์˜์—ญ์—์„œ ์ •๋ณด ์†์‹ค์ด ๋ฐœ์ƒํ•œ๋‹ค๋Š” ์ ์ด๋‹ค. ๊ณผ๊ฑฐ์˜ ๋ฐฉ๋ฒ•๋“ค์ด ๊ณ ์ „์ ์ธ ์ด๋ฏธ์ง€ ์ •๋ ฌ ๋ฐฉ๋ฒ•๋“ค(e.g., homography, optical flow)์„ ์‚ฌ์šฉํ•˜์—ฌ ์ž…๋ ฅ LDR ์ด๋ฏธ์ง€๋“ค์„ ์ „์ฒ˜๋ฆฌ ๊ณผ์ •์—์„œ ์ •๋ ฌํ•˜ ์—ฌ ๋ณ‘ํ•ฉํ•˜๋Š” ์‹œ๋„๋ฅผ ํ–ˆ์ง€๋งŒ, ์ด ๊ณผ์ •์—์„œ ๋ฐœ์ƒํ•˜๋Š” ์ถ”์ • ์˜ค๋ฅ˜๋กœ ์ธํ•ด ์ดํ›„ ๋‹จ๊ณ„์— ์•…์˜ํ•ญ์„ ๋ฏธ์นจ์œผ๋กœ์จ ๋ฐœ์ƒํ•˜๋Š” ์—ฌ๋Ÿฌ๊ฐ€์ง€ ๋ถ€์ ์ ˆํ•œ ์•„ํ‹ฐํŒฉํŠธ๋“ค์ด ๊ฒฐ๊ณผ HDR ์ด๋ฏธ์ง€์—์„œ ๋‚˜ํƒ€๋‚˜๊ณ  ์žˆ๋‹ค. ๋ณธ ์‹ฌ์‚ฌ์—์„œ๋Š” ํ”ผ์ณ ๋ถ„ํ•ด๋ฅผ ์‘์šฉํ•œ HDR ๋„คํŠธ์›Œํฌ๋ฅผ ์ œ์•ˆํ•˜์—ฌ, ์–ธ๊ธ‰๋œ ๋ฌธ์ œ๋“ค์„ ๊ฒฝ๊ฐํ•˜๊ณ ์ž ํ•œ๋‹ค. ๊ตฌ์ฒด์ ์œผ๋กœ, ๋จผ์ € LDR ์ด๋ฏธ์ง€๋“ค์„ ๋…ธ์ถœ ํ”ผ์ณ์™€ ๊ณต๊ฐ„ ํ”ผ์ณ๋กœ ๋ถ„ํ•ดํ•˜๊ณ , ๋ถ„ํ•ด๋œ ํ”ผ์ณ๋ฅผ HDR ๋„คํŠธ์›Œํฌ์—์„œ ํ™œ์šฉํ•จ์œผ๋กœ์จ ๊ณ ํ’ˆ์งˆ์˜ HDR ์ด๋ฏธ์ง€ ๋ฅผ ์ƒ์„ฑํ•  ์ˆ˜ ์žˆ๋„๋ก ํ•œ๋‹ค. ์ œ์•ˆํ•œ ๋„คํŠธ์›Œํฌ๋Š” ์„ฑ๋Šฅ ์ง€ํ‘œ์ธ PSNR-โ„“๊ณผ PSNR-ฮผ์—์„œ ๊ฐ๊ฐ 41.89dB, 44.19dB์˜ ์„ฑ๋Šฅ์„ ๋‹ฌ์„ฑํ•จ์œผ๋กœ์จ, ๊ธฐ์กด ๋ฐฉ๋ฒ•๋“ค๋ณด๋‹ค ์šฐ์ˆ˜ํ•จ์„ ์ž…์ฆํ•œ๋‹ค.1 Introduction 1 2 Related Works 4 2.1 Single-frame HDR imaging 4 2.2 Multi-frame HDR imaging with dynamic scenes 6 3 Proposed Method 10 3.1 Disentangle Network for Feature Extraction 10 3.2 Disentangle Features Guided Network 16 4 Experimental Results 22 4.1 Implementation and Details 22 4.2 Comparison with State-of-the-art Methods 22 5 Ablation Study 30 5.1 Impact of Proposed Modules 30 6 Conclusion 32 Abstract (In Korean) 39์„
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