Image Compression by Learning to Minimize the Total Error

Zhang, Chiyuan; He, Xiaofei

doi:10.1109/tcsvt.2012.2210803

Cited by 18 publications

(25 citation statements)

References 20 publications

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“…Zhang et al [4], the compression involves storing only the grayscale image and a few carefully selected color pixel seeds. For decompression, regression models are learned with the stored data to predict the missing colors.…”

Section: International Journal Of Computer Applications (0975 -8887) mentioning

confidence: 99%

“…The initial step of evaluation finds total all scale-space and different image area in image dataset nodes [4]. It is completely apply effectively by using a Difference-of-Gaussian (DoG) mapping to represents potential interest keypoints of feature descriptors which are scale invariant and orientation in image dataset nodes [6].…”

Section: Scale-space Extreme Detectionmentioning

confidence: 99%

“…Keypoints Descriptor: The local image gradients value are measured at the choose scale-space in the Region of Interest (ROI) around all keypoints in image dataset points [4]. These are transformed into a presentation that permits for significant levels of local shape, location and orientation and changes in illumination of image dataset points [6].…”

Section: Scale-space Extreme Detectionmentioning

confidence: 99%

See 2 more Smart Citations

An Efficient Image Compression using Singular Value Decomposition with Scale Invariant Feature Transform

Pandey¹,

Silhare²

2017

IJCA

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We propose a novel compression scheme for digital images using chrominance channels with feature descriptor and Singular Value Decomposition (SVD). The feature descriptor of chrominance channel is based on their error metrics. We describe an input image based on its down-sampled version and local feature descriptors. The chrominance channel descriptors are used to retrieve feature descriptive images and identify corresponding patches. The down-sampled image serves as a target to stitch retrieved image patches together. The feature vectors of local descriptors are predicted by the corresponding vectors extracted in the decoded down-sampled image. The image is decomposed by using SVD and then the rank is being reduced by ignoring some of the lower singular values as well as rows of hanger and aligner matrices. Experimental results demonstrate the effectiveness of the proposed scheme. The overall compression process supports to reach a acceptable level for image transmission in limited bandwidth over a telecommunication medicine application.

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Section: International Journal Of Computer Applications (0975 -8887) mentioning

confidence: 99%

Section: Scale-space Extreme Detectionmentioning

confidence: 99%

Section: Scale-space Extreme Detectionmentioning

confidence: 99%

See 1 more Smart Citation

An Efficient Image Compression using Singular Value Decomposition with Scale Invariant Feature Transform

Pandey¹,

Silhare²

2017

IJCA

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“…Experimental results demonstrated the outstanding performance of the proposed methods. Although the computation burden is still high, TEM-C is already competitive to the industrial standard JPEG in image quality and compression ratio [4].…”

Section: Linear Discriminant Analysis (Lda)mentioning

confidence: 99%

Image Compression Methods using Dimension Reduction and Classification through PCA and LDA: A Review

2016

IJSR

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“…The predicted residual vectors are compressed by transform, quantization, and entropy coding. Zhang et al [4], the compression involves storing only the grayscale image and a few carefully selected color pixel seeds. For decompression, regression models are learned with the stored data to predict the missing colors.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Image Compression using Discrete Cosine Transform with SIFT and Modified Colorization Channel

Kumar¹

2017

IJRASET

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: We propose a novel compression scheme for digital images using chrominance channels with feature descriptor and Discrete Cosine Transform (DCT). The feature descriptor of chrominance channel is based on their error metrics. We describe an input image based on its down-sampled version and local feature descriptors. The chrominance channel descriptors are used to retrieve feature descriptive images and identify corresponding patches. The down-sampled image serves as a target to stitch retrieved image patches together. The feature vectors of local descriptors are predicted by the corresponding vectors extracted in the decoded down-sampled image. The image is transformed by using DCT and then the rank is being reduced by ignoring some of the lower singular values as well as rows of hanger and aligner matrices. Experimental results demonstrate the effectiveness of the proposed scheme. The overall compression process supports to reach a acceptable level for image transmission in limited bandwidth over a telecommunication medicine application. We analyzed the performance of image compression technique using

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Image Compression by Learning to Minimize the Total Error

Cited by 18 publications

References 20 publications

An Efficient Image Compression using Singular Value Decomposition with Scale Invariant Feature Transform

An Efficient Image Compression using Singular Value Decomposition with Scale Invariant Feature Transform

Image Compression Methods using Dimension Reduction and Classification through PCA and LDA: A Review

An Efficient Image Compression using Discrete Cosine Transform with SIFT and Modified Colorization Channel

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