Critically sampled graph-based wavelet transforms for image coding

Narang, Sunil K.; Chao, Yung‐Hsuan; Ortega, Antonio

doi:10.1109/apsipa.2013.6694319

Cited by 20 publications

(14 citation statements)

References 13 publications

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“…One of the advantages of the critically sampled two‐channel graph‐based wavelet transform is that the number of transform coefficients is the same as the number of image pixels. Thus this critically sampled transform has been used in image compression [39]. However, as mentioned in the work [10], the overcomplete wavelet decomposition is much better than the complete wavelet transformation for texture analysis.…”

Section: Feature Extraction and Classificationmentioning

confidence: 99%

Graph wavelet transform for image texture classification

Qiao

Zhao

Song

et al. 2021

IET Image Processing

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Graph is a data structure that can represent complex relationships among data. Graph signal processing, unlike traditional signal processing, explicitly considers the structure and relationship among the signal samples. Graph wavelet transform can provide a multiscale analysis for the graph signal. It is well known that texture is a region property in an image, which is characterized with the intensity and relationship among pixels. In this context of the graph signal processing framework, an image texture can be considered as the signal on the graph. Therefore, a texture classification method based on graph wavelet transform is proposed. Specifically, image textures are decomposed into multiscale components by using two-channel graph wavelet filter banks. Then the local singular value decomposition is applied to each subband. In order to improve the noise-resistant ability, the maximum, mean and median values of the local singular values of graph-wavelet transformation coefficients are extracted. Finally, the Weibull distributions are used to model those extracted values to describe the image textures. The experiments on the benchmark texture datasets are conducted to demonstrate the effectiveness of the proposed method.How to cite this article: Qiao Y-L, Zhao Y, Song C-Y, Zhang K-G, Xiang X-Z. Graph wavelet transform for image texture classification.

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Section: Feature Extraction and Classificationmentioning

confidence: 99%

Graph wavelet transform for image texture classification

Qiao

Zhao

Song

et al. 2021

IET Image Processing

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“…Finally, a few works have employed graph wavelets to image/video coding problems. In [81] a graph wavelet transform has been proposed for image compression. In [82]- [84] the authors propose a complete video encoder based on liftingbased wavelet transforms on graphs; constructing a graph in which any pixel could be linked to several spatial and temporal neighbors, they jointly exploit spatial and temporal correlation.…”

Section: Applicationsmentioning

confidence: 99%

Graph Spectral Image Processing

et al. 2018

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Recent advent of graph signal processing (GSP) has spurred intensive studies of signals that live naturally on irregular data kernels described by graphs (e.g., social networks, wireless sensor networks). Though a digital image contains pixels that reside on a regularly sampled 2D grid, if one can design an appropriate underlying graph connecting pixels with weights that reflect the image structure, then one can interpret the image (or image patch) as a signal on a graph, and apply GSP tools for processing and analysis of the signal in graph spectral domain. In this article, we overview recent graph spectral techniques in GSP specifically for image / video processing. The topics covered include image compression, image restoration, image filtering and image segmentation.

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“…Several block based compression methods using GFT have been proposed [8][9][10]. In [11,12] GFT is extended to provide a multiresolution representation. These works propose to use GFT for compression of piece-wise smooth data, e.g.…”

Section: Introductionmentioning

confidence: 99%

Directional graph weight prediction for image compression

Verdoja

Grangetto

2017

2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Graph-based models have recently attracted attention for their potential to enhance transform coding image compression thanks to their capability to efficiently represent discontinuities. Graph transform gets closer to the optimal KLT by using weights that represent inter-pixel correlations but the extra cost to provide such weights can overwhelm the gain, especially in the case of natural images rich of details. In this paper we provide a novel idea to make graph transform adaptive to the actual image content, avoiding the need to encode the graph weights as side information. We show that an approach similar to spatial prediction can be used to effectively predict graph weights in place of pixels; in particular, we propose the design of directional graph weight prediction modes and show the resulting coding gain. The proposed approach can be used jointly with other graph based intra prediction methods to further enhance compression. Our comparative experimental analysis, carried out with a fully fledged still image coding prototype, shows that we are able to achieve significant coding gains.

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Critically sampled graph-based wavelet transforms for image coding

Cited by 20 publications

References 13 publications

Graph wavelet transform for image texture classification

Graph wavelet transform for image texture classification

Graph Spectral Image Processing

Directional graph weight prediction for image compression

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