Scalable texture compression using the wavelet transform

Andries, Bob; Lemeire, Jan; Munteanu, Adrian

doi:10.1007/s00371-016-1269-1

Cited by 3 publications

(5 citation statements)

References 14 publications

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“…Fast decoding of compressed textures was demonstrated by Andries et al [8], reporting GPU illrates in the order of tens of gigapixels per second. The illrate of NVIDIA GTX 980 GPU used by the authors is 144 gigatexels per second which can be seen as a theoretical limit to the decoding performance.…”

Section: Discussionmentioning

confidence: 99%

“…Andries et al [8], expanding previous work [7], proposed wavelet transform as an improvement to traditional texture compression formats. The wavelet coeicients are packed into a regular texture compression format and reconstructed on the decoding side in a shader.…”

Section: Implementations Mentioning Decoding Speedmentioning

confidence: 99%

“…b) This example uses BC1 compression in an in-home streaming system for 5K (5120 × 2880) video developed by Pohl et al [113]. Since the decompression speed for BC1 was not reported during the survey, we can use the fastest reported throughput 25 gigapixels per second of wavelet texture compression by Andries et al [8] and consider it as a conservative estimate. By upgrading the network to a future 5G standard, it is possible to reduce the theoretical latency by more than twice.…”

Section: Theoretical Achievable Latency Examplesmentioning

confidence: 99%

“…Software implementations of texture encoding for resolutions higher than 4K use high-end desktop computers [60] and multi-server setups [113], although most of the processing power of the latter was used for rendering. Hardware GPU texture decompression was shown to be extremely fast, even with a custom restoration shader overhead [8]. The only hardware (FPGA) implementation of texture encoding [165] shows very low performance but also very low resource usage.…”

Section: Summary Of Reviewed Implementationsmentioning

confidence: 99%

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Image and Video Coding Techniques for Ultra-low Latency

et al. 2022

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The next generation of wireless networks fosters the adoption of latency-critical applications such as XR, connected industry, or autonomous driving. This survey gathers implementation aspects of different image and video coding schemes and discusses their tradeoffs. Standardized video coding technologies such as HEVC or VVC provide a high compression ratio, but their enormous complexity sets the scene for alternative approaches like still image, mezzanine, or texture compression in scenarios with tight resource or latency constraints. Regardless of the coding scheme, we found inter-device memory transfers and the lack of sub-frame coding as limitations of current full-system and software-programmable implementations.

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Section: Discussionmentioning

confidence: 99%

Section: Implementations Mentioning Decoding Speedmentioning

confidence: 99%

Section: Theoretical Achievable Latency Examplesmentioning

confidence: 99%

Section: Summary Of Reviewed Implementationsmentioning

confidence: 99%

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Image and Video Coding Techniques for Ultra-low Latency

et al. 2022

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“…Although the lossless compression is often used to preserve the full information of original images, it provides a lower compression ratio than that of lossy compression, which is a disadvantage for channel transmission and storage. For the last few years, discrete wavelet transform (DWT) has been commonly used for image compression and restoration [1][2][3][4] . It is a robust technique for image compression due to its multiresolution feature, scalability and flexibility.…”

Section: Introductionmentioning

confidence: 99%

Support vector regression-based 3D-wavelet texture learning for hyperspectral image compression

2019

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Hyperspectral imaging is known for its rich spatial-spectral information. The spectral bands provide the ability to distinguish substances spectra which is substantial for analyzing materials. However, highdimensional data volume of hyperspectal images is problematic for data storage. In this paper, we present a lossy hyperspectral image compression system based on the regression of 3D wavelet coefficients. The 3D wavelet transform is applied to sparsely represent the hyperspectral images (HSI). A support vector machine regression (SVR) is then applied on wavelet details and provides vector supports and weights which represent wavelet texture features. To achieve the best possible overall rate-distortion performance after regression, entropy encoding based on run-length encoding (RLE) and arithmetic encoding are used. To preserve the spatial pertinent information of the image, the lowest subband wavelet coefficients are furthermore encoded by a lossless coding with differential pulse code modulation (DPCM). Spectral and spatial redundancies are thus substantially reduced. Experimental tests are performed over several hyperspectral images from airborne and spaceborne sensors and compared with the main existing algorithms. The obtained results show that the proposed compression method has high performances in terms of rate-distortion and spectral fidelity. Indeed, high PSNRs and classification accuracies, which could exceed 40.65 dB and 75.8 % respectively, are observed for all decoded HSI images and overpass those given by

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Wavelet-Based ${L_\infty }$ Semi-regular Mesh Coding

Florea

Munteanu

et al. 2017

IEEE Trans. Multimedia

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Scalable texture compression using the wavelet transform

Cited by 3 publications

References 14 publications

Image and Video Coding Techniques for Ultra-low Latency

Image and Video Coding Techniques for Ultra-low Latency

Support vector regression-based 3D-wavelet texture learning for hyperspectral image compression

Wavelet-Based ${L_\infty }$ Semi-regular Mesh Coding

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