High dynamic range texture compression for graphics hardware

Munkberg, Jacob; Clarberg, Petrik; Hasselgren, Jon; Akenine-Möller, Tomas

doi:10.1145/1141911.1141944

Cited by 46 publications

(44 citation statements)

References 24 publications

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“…To extend the range of LDR metrics and to consider the sensitivity of the HVS, Aydin et al [20] have proposed the perceptually uniform (PU) encoding. Another approach to apply LDR metrics on HDR images was proposed in [21]. This technique consists in tone-mapping the HDR image to several LDR images with different exposure ranges and to take the average objective score computed on each exposure.…”

Section: Introductionmentioning

confidence: 99%

Benchmarking of objective quality metrics for HDR image quality assessment

Hanhart

Bernardo

Pereira

et al. 2015

J Image Video Proc.

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Recent advances in high dynamic range (HDR) capture and display technologies have attracted a lot of interest from scientific, professional, and artistic communities. As in any technology, the evaluation of HDR systems in terms of quality of experience is essential. Subjective evaluations are time consuming and expensive, and thus objective quality assessment tools are needed as well. In this paper, we report and analyze the results of an extensive benchmarking of objective quality metrics for HDR image quality assessment. In total, 35 objective metrics were benchmarked on a database of 20 HDR contents encoded with 3 compression algorithms at 4 bit rates, leading to a total of 240 compressed HDR images, using subjective quality scores as ground truth. Performance indexes were computed to assess the accuracy, monotonicity, and consistency of the metric estimation of subjective scores. Statistical analysis was performed on the performance indexes to discriminate small differences between metrics. Results demonstrated that metrics designed for HDR content, i.e., HDR-VDP-2 and HDR-VQM, are the most reliable predictors of perceived quality. Finally, our findings suggested that the performance of most full-reference metrics can be improved by considering non-linearities of the human visual system, while further efforts are necessary to improve performance of no-reference quality metrics for HDR content.

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Section: Introductionmentioning

confidence: 99%

Benchmarking of objective quality metrics for HDR image quality assessment

Hanhart

Bernardo

Pereira

et al. 2015

J Image Video Proc.

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“…One may also notice that, due to the tone-mapping effect, the close-up shots extracted from Room image present some aliasing effects and artifacts in color transitions. To evaluate the effectiveness of our compression method in the case of HDR reconstruction, we use the Multi-Exposure Peak Signal Noise Ratio (mPSNR) [13], which is a popular objective quality metric for assessing HDR compression methods. For a given HDR image, several tone mapped versions with different exposure parameter values, are created, and then PSNR measures are calculated for these tone-mapped versions.…”

Section: Performance Resultsmentioning

confidence: 99%

HDR image compression with optimized JPEG coding

Zaid

Houimli²

2017

2017 25th European Signal Processing Conference (EUSIPCO)

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Abstract-This paper presents an efficient compression system adapted to High Dynamic Range (HDR) images. First a Tone Mapping Operator (TMO) generates the Low Dynamic Range (LDR) version of the HDR content together with its extra information. The obtained LDR image is encoded using an optimized JPEG coding scheme, whereas the extra information is encoded as side data. Specifically, the optimized JPEG based algorithm constructs near-optimal rate-distortion quantization tables using DCT coefficient distribution statistics and Lagrangian optimization approach. To ensure accurate HDR reconstruction, the extra information is compressed with conventional JPEG encoder using the highest quality level. The aim of the proposed HDR coding system is twofold. First, it performs a bit allocation mechanism, to achieve near-optimal rate control. Second, it maintains the backward compatibility with the conventional JPEG. Experiments show that the compression performance of the proposed HDR coder outperforms that of the reference method.

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“…Despite proposals for future hardware design [Munkberg et al 2006;Roimela et al 2006], current generation GPUs do not provide adequate support for compressed HDR textures. We provide a solution to address this issue.…”

Section: Discussionmentioning

confidence: 99%

“…In particular, both [Munkberg et al 2006;Roimela et al 2006] concentrate HDR information into one luminance channel for easy processing.…”

Section: Color Space Conversionmentioning

confidence: 99%

“…This makes the size disparity even bigger between compressed 8-bit and uncompressed HDR textures. Recently, various extensions have been proposed for supporting compressed HDR textures on graphics hardware [Munkberg et al 2006;Roimela et al 2006]. However, these solutions are not immediately available, as the proposed extensions are not part of current (DX9) or even future (DX10) graphics APIs or hardware.…”

Section: Introductionmentioning

confidence: 99%

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Rendering from compressed high dynamic range textures on programmable graphics hardware

Wang

Sloan

et al. 2007

Proceedings of the 2007 Symposium on Interactive 3D Graphics and Games

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High dynamic range (HDR) images are increasingly employed in games and interactive applications for accurate rendering and illumination. One disadvantage of HDR images is their large data size; unfortunately, even though solutions have been proposed for future hardware, commodity graphics hardware today does not provide any native compression for HDR textures.In this paper, we perform extensive study of possible methods for supporting compressed HDR textures on commodity graphics hardware. A desirable solution must be implementable on DX9 generation hardware, as well as meet the following requirements. First, the data size should be small and the reconstruction quality must be good. Second, the decompression must be efficient; in particular, bilinear/trilinear/anisotropic texture filtering ought to be performed via native texture hardware instead of custom pixel shader filtering.We present a solution that optimally meets these requirements. Our basic idea is to convert a HDR texture to a custom LUVW space followed by an encoding into a pair of 8-bit DXT textures. Since DXT format is supported on modern commodity graphics hardware, our approach has wide applicability. Our compression ratio is 3:1 for FP16 inputs, allowing applications to store 3 times the number of HDR texels in the same memory footprint. Our decompressor is efficient and can be implemented as a short pixel program. We leverage existing texturing hardware for fast decompression and native texture filtering, allowing HDR textures to be utilized just like traditional 8-bit DXT textures. Our reduced data size has a further advantage: it is even faster than rendering from uncompressed HDR textures due to our reduced texture memory access. Given the quality and efficiency, we believe our approach suitable for games and interactive applications.

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High dynamic range texture compression for graphics hardware

Cited by 46 publications

References 24 publications

Benchmarking of objective quality metrics for HDR image quality assessment

Benchmarking of objective quality metrics for HDR image quality assessment

HDR image compression with optimized JPEG coding

Rendering from compressed high dynamic range textures on programmable graphics hardware

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