Hadamard transform domain filter for video coding

Ikonin, Sergey; Stepin, Victor; Chernyak, Roman; Chen, Jianle

doi:10.1117/12.2530513

Cited by 3 publications

(2 citation statements)

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“…It has some restrictions in terms of the components to which it can be applied and the requirements that must be met for its implementation. Ikonin et al [21], a filter in the Hadamard transform domain is proposed. The filter is applied directly to the decoded samples at the block level during reconstruction.…”

Section: Literature Reviewmentioning

confidence: 99%

Optimized in-loop filtering in versatile video coding using improved fast guided filter

Amrutha Valli Pamidi,

Nalluri

2024

IJEECS

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<p>Devices with varying display capabilities from a common source may face degradation in video quality because of the limitation in transmission bandwidth and storage. The solution to overcome this challenge is to enrich the video quality. For the mentioned purpose, this paper introduces an improved fast guided filter (IFGF) for the contemporary video coding standard H.266/VVC (versatile video coding), a continuation of H.265/HEVC (high efficiency video coding). VVC includes several types of coding techniques to enhance video coding efficiency over existing video coding standards. Despite that, blocking artifacts are still present in the images. Hence, the proposed method focuses on denoising the image and the increase of video quality, which is measured in terms of peak signal-to-noise (PSNR). The objective is achieved by using an IFGF for in-loop filtering in VVC to denoise the reconstructed images. VTM (VVC test model)-17.2 is used to simulate the various video sequences with the proposed filter. This method achieves a 0.67% Bjontegaard delta (BD)-rate reduction in low-delay configuration accompanied by an encoder run time increase of 4%.</p>

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Section: Literature Reviewmentioning

confidence: 99%

Optimized in-loop filtering in versatile video coding using improved fast guided filter

Amrutha Valli Pamidi,

Nalluri

2024

IJEECS

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“…In these both examples there is one bit of information transmitted per every coding tree unit (CTU), which indicates whether the filter is enabled in the current CTU or not. As another example, Bilateral Filter [8], which was studied during VVC standardization process, and Hadamard Transform Domain Filter (HTDF) [9] from current EVC standard draft do not have an explicit mechanism of the filters enabling. In these two cases, codec has some predefined rules of the filter applicability such as minimal block size etc., and always applies the filter for all blocks satisfying the rules.…”

Section: Introductionmentioning

confidence: 99%

Application Map in the Noise Suppression Filter for Video Compression

Chernyak

Meshcheryakov

2020

Proceedings of the 30th International Conference on Computer Graphics and Machine Vision (GraphiCon 2020). Part 2

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This paper considers an application map method usage together with the Nose Suppression Filter (NSF) in-loop filter for video compression. The application map method is described in details and simulations results of basic NSF are provided as well as simulation results of NSF powered with the application map method. It is demonstrated that the application map method allows to significantly improve objective performance results of basic NSF and additionally decrease decoder average complexity. As a result, an average bd-rate saving of NSF with the application map reaches 2.6% for luma component in Low Delay P coding configuration in comparing to Versatile Video Coding reference implementation (VTM) version 1.0.

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DCT and DST Filtering With Sparse Graph Operators

Ortega

Mukherjee

et al. 2022

IEEE Trans. Signal Process.

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Hadamard transform domain filter for video coding

Cited by 3 publications

References 2 publications

Optimized in-loop filtering in versatile video coding using improved fast guided filter

Optimized in-loop filtering in versatile video coding using improved fast guided filter

Application Map in the Noise Suppression Filter for Video Compression

DCT and DST Filtering With Sparse Graph Operators

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