A hybrid DWT-SVD image-coding system (HDWTSVD) for monochromatic images

Ochoa, Humberto; Rao, K. R.

doi:10.1117/12.476715

Cited by 11 publications

(6 citation statements)

References 10 publications

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“…In this research, a new system that combines techniques of DWT and SVD has been presented. Previous results to this work were published in [13], [14]. In this research, better quality images for both low and high bit rates have been obtained by optimizing each stage of the hybrid system.…”

Section: Discussionmentioning

confidence: 90%

A discrete wavelet transform-singular value decomposition system for image coding

Domínguez

Rao²

2007

JART

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A system that combines techniques of wavelet transform (DWT) and singular value decomposition (SVD) to encode images is presented. The image is divided into tiles or blocks of 64x64 pixels. The decision criterion as to which transform to use is based on the standard deviation of the 8x8 pixel subblocks of the tile to encode. A successive approximation quantizer is used to encode the subbands and vector quantization/scalar quantization is used to encode the SVD eigenvectors/eigenvalues, respectively. For coding color images, the RGB components are transformed into YCbCr before encoding in 4:2:0 format. Results show that the proposed system outperforms the JPEG and approaches the JPEG2000.

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Section: Discussionmentioning

confidence: 90%

A discrete wavelet transform-singular value decomposition system for image coding

Domínguez

Rao²

2007

JART

Self Cite

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“…Compared with the 1D-SVD codecs [7,8], the proposed 2D-SVD codec inherits the energy compaction property from a 1D-SVD scheme, while it needs to code and transmit much fewer coefficients. In Section 2 of this paper, the 2D-SVD and its characteristics relevant to our work are to be firstly introduced.…”

Section: Introductionmentioning

confidence: 98%

“…This coefficient matrix contains much less non-zero coefficients compared with the coefficient matrices of other transformations. However, the 1D-SVD based coding techniques typically achieve only modest compression because the eigenvectors must be coded along with the associated eigenvalues [7,8].…”

Section: Introductionmentioning

confidence: 99%

Two dimensional Singular Value Decomposition (2D-SVD) based video coding

Wang

Lee

et al. 2010

2010 IEEE International Conference on Image Processing

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In this paper, we propose a low-complexity video codec based on two-dimensional Singular Value Decomposition (2D-SVD). We exploit the common temporal characteristics of video without resorting to motion estimation. It has been demonstrated that this codec has higher coding efficiency than the relevant existing low complexity codecs. Moreover, the proposed codec performs well to deal with packet loss that is unavoidable in error-prone transmission. Therefore it is with advantages and good potential for wireless video applications such as mobile video calls and wireless surveillance.

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“…At the first glance, applying LRMA to data compression seems to be straightforward, since one only needs to store k(m + n) elements, with small approximation error introduced in LRMA. Such an idea has been used extensively to compress various types of data, e.g., images/videos [2], [16], [17], [18], [19], [20], [3], 3D motion data [21], [22], [23], [24], [25], traffic data [26], [27], [28]. However, data samples usually exhibit both intra-coherence (i.e., coherence within each data sample) and inter-coherence (i.e., coherence among different data samples).…”

mentioning

confidence: 99%

Sparse Low-Rank Matrix Approximation for Data Compression

Hou

Chau

Thalmann

et al. 2017

IEEE Trans. Circuits Syst. Video Technol.

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Low-rank matrix approximation (LRMA) is a powerful technique for signal processing and pattern analysis. However, its potential for data compression has not yet been fully investigated in the literature. In this paper, we propose sparse lowrank matrix approximation (SLRMA), an effective computational tool for data compression. SLRMA extends the conventional LRMA by exploring both the intra-and inter-coherence of data samples simultaneously. With the aid of prescribed orthogonal transforms (e.g., discrete cosine/wavelet transform and graph transform), SLRMA decomposes a matrix into a product of two smaller matrices, where one matrix is made of extremely sparse and orthogonal column vectors, and the other consists of the transform coefficients. Technically, we formulate SLRMA as a constrained optimization problem, i.e., minimizing the approximation error in the least-squares sense regularized by ℓ0norm and orthogonality, and solve it using the inexact augmented Lagrangian multiplier method. Through extensive tests on realworld data, such as 2D image sets and 3D dynamic meshes, we observe that (i) SLRMA empirically converges well; (ii) SLRMA can produce approximation error comparable to LRMA but in a much sparse form; (iii) SLRMA-based compression schemes significantly outperform the state-of-the-art in terms of ratedistortion performance.

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A hybrid DWT-SVD image-coding system (HDWTSVD) for monochromatic images

Cited by 11 publications

References 10 publications

A discrete wavelet transform-singular value decomposition system for image coding

A discrete wavelet transform-singular value decomposition system for image coding

Two dimensional Singular Value Decomposition (2D-SVD) based video coding

Sparse Low-Rank Matrix Approximation for Data Compression

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