Image coding using the singular value decomposition and vector quantization

McGoldrick, Ciarán; Dowling, William J.; Bury, A.

doi:10.1049/cp:19950668

Cited by 26 publications

(15 citation statements)

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“…The SVD is one of the most useful tools of linear algebra with several applications to multimedia including image compression [17,18,19,20,21,22,23,24,25], signal processing [26,27,28,29], and watermarking [30,31,32]. The watermarked image is then constructed as:…”

Section: Every Real Matrixmentioning

confidence: 99%

A hybrid scheme for encryption and watermarking

Dexter

Eskicioğlu

2004

SPIE Proceedings

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Encryption and watermarking are complementary lines of defense in protecting multimedia content. Recent watermarking techniques have therefore been developed independent from encryption techniques. In this paper, we present a hybrid image protection scheme to establish a relation between the data encryption key and the watermark. Prepositioned secret sharing allows the reconstruction of different encryption keys by communicating different activating shares for the same prepositioned information. Each activating share is used by the receivers to generate a fresh content decryption key. In the proposed scheme, the activating share is used to carry copyright or usage rights data. The bit stream that represents this data is also embedded in the content as a visual watermark. When the encryption key needs to change, the data source generates a new activating share, and embeds the corresponding watermark into the multimedia stream. Before transmission, the composite stream is encrypted with the key constructed from the new activating share. Each receiver can decrypt the stream after reconstructing the same key, and extract the watermark from the image. Our presentation will include the application of the scheme to a test image, and a discussion on the data hiding capacity, watermark transparency, and robustness to common attacks.

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Section: Every Real Matrixmentioning

confidence: 99%

A hybrid scheme for encryption and watermarking

Dexter

Eskicioğlu

2004

SPIE Proceedings

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“…The use of Singular Value Decomposition (SVD) in image compression has been widely studied [1,3,9,10]. If the image, when considered as a matrix, has low rank, or can be approximated sufficiently well by a matrix of low rank, then SVD can be used to find this approximation, and further this low rank approximation can be represented much * Currently at Symantec Corporation, Pune, India.…”

Section: Introductionmentioning

confidence: 99%

“…We expect that a similar array of ideas, for each level of the process, will have to be developed if SSVD is to compete with JPEG. Vector quantization is one idea that has been considered in the literature [3,8,9,10]. In this paper we report on another strategy.…”

Section: Introductionmentioning

confidence: 99%

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A variation on SVD based image compression

Ranade

Mahabalarao

Kale

2007

Image and Vision Computing

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“…in [Andrews and Patterson (1976)], [Chi8ariglione eet al (1979),] [Goldrick et al (1995)], [Saito and Komatsu (1990)], [Uuang and Ln (19995)], [Waldemar and Ramstad (1996), [Waldemar and Ramstad (ICASSP, 1997)], Waldemar and Ramsstad (NORSIG, 1997)]and [WaldemarH. D Thesis 1997)].…”

Section: Introductionmentioning

confidence: 99%

Compression of seismic stack sections using singular value decomposition

Waldemar

Røsten

Ramstad³

1997

SEG Technical Program Expanded Abstracts 1997

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Singular value decomposition (SVD) is an adaptive transform with excellent energy compaction properties for separable data. Hence, applying SVD on seismic stack sections seems to be a good choice. In order to achieve a good SVD compression system, bit-efficient representations must be found for both the transform (singular vectors) and the transform coefficients (singular values). In this paper we present an SVD coding system which exploits the orthonormality of the singular vectors to find a minimum number of parameter representation for the singular vectors. These parameters and the singular values are quantized using pdf optimized quantizers. The SVD coder is designed to give full control of the mse within each image block. The proposed method has the ability to compress seismic stack sections at a ratio of 30:1 with no loss of visual information. Regarding the novelty of this scheme this must be considered to be a good result, although it is outperformed by a standard JPEG image coder in terms of signal to noise ratio.

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Image coding using the singular value decomposition and vector quantization

Cited by 26 publications

References 0 publications

A hybrid scheme for encryption and watermarking

A hybrid scheme for encryption and watermarking

A variation on SVD based image compression

Compression of seismic stack sections using singular value decomposition

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