Colorization-based coding by focusing on characteristics of colorization bases

Ono, Shunsuke; Miyata, Takamichi; Sakai, Yoshinori

doi:10.1109/pcs.2010.5702473

Cited by 25 publications

(16 citation statements)

References 4 publications

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“…Next this paper shows the coding performance of the proposed algorithm comparing with the algorithm proposed in [9] and the random algorithm which selects RPs randomly. We chose λ = 5 in (12), which is selected empirically from λ ∈[ 1, 10] to achieve the best performance.…”

Section: Numerical Examplesmentioning

confidence: 99%

“…The development of the colorization technique has led to the generation of a new color image coding method called colorization-based color image coding [5][6][7][8][9][10][11][12]. Because the luminance image can be compressed by standard coding methods such as JPEG coding and because the chrominance information is represented by a small number of RPs, the colorization-based image coding can compress full-color images more than these methods.…”

Section: Introductionmentioning

confidence: 99%

“…In order to reduce the amount of information to represent the RPs, the method [5] assumes that all pixels in a line segment have only one color and proposes the algorithm where the RPs are described as a set of color line segments. In [9], the algorithm finds a set of two RPs which give the similar effect for colorization result, and the redundant RPs are deleted. In [12], under the assumption that the chrominance image is given as a sparse linear combination of the basis constructed from the luminance image, the algorithm achieves a high compression performance using the theory of compressed sensing [13,14].…”

Section: Introductionmentioning

confidence: 99%

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Fast colorization based image coding algorithm using multiple resolution images

Uruma

Konishi

Takahashi

et al. 2016

J Image Video Proc.

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This paper proposes a representative pixel (RP) extraction algorithm and chrominance image recovery algorithm for the colorization-based digital image coding. The colorization-based coding methods reduce the color information of an image and achieve higher compression ratio than JPEG coding; however, they take much more computing time. In order to achieve low computational cost, this paper proposes the algorithm using the set of multiple-resolution images obtained by colorization error minimizing method. This algorithm extracts RPs from each resolution image and colorizes each resolution image utilizing a lower resolution color image, which leads to the reduction of the number of RPs and computing time. Numerical examples show that the proposed algorithm extracts the RPs and recovers the color image fast and effectively.

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Section: Numerical Examplesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Fast colorization based image coding algorithm using multiple resolution images

Uruma

Konishi

Takahashi

et al. 2016

J Image Video Proc.

View full text Add to dashboard Cite

show abstract

“…Recently, several colorization-based coding methods have been proposed [1]- [5]. Colorization is the process of adding color to a grayscale image using scant color information, which is called representative pixels (RP).…”

Section: Introductionmentioning

confidence: 99%

“…Colorization can be formulated as a process that applies a colorization matrix to a vector which consists of RP. In the previous colorization-based coding methods [2]- [5] using Levin's colorization method [6], a colorization matrix is constructed after RP extraction. These methods iteratively add or reduce RP to find a better RP set which reduces the reconstruction error between an original color component and its reconstructed color component.…”

Section: Introductionmentioning

confidence: 99%

Improvement of Colorization-Based Coding Using Optimization by Novel Colorization Matrix Construction and Adaptive Color Conversion

Mishiba

Yoshitome

2015

IEICE Trans. Inf. & Syst.

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SUMMARYThis study improves the compression efficiency of Lee's colorization-based coding framework by introducing a novel colorization matrix construction and an adaptive color conversion. Colorization-based coding methods reconstruct color components in the decoder by colorization, which adds color to a base component (a grayscale image) using scant color information. The colorization process can be expressed as a linear combination of a few column vectors of a colorization matrix. Thus it is important for colorization-based coding to make a colorization matrix whose column vectors effectively approximate color components. To make a colorization matrix, Lee's colorization-based coding framework first obtains a base and color components by RGB-YCbCr color conversion, and then performs a segmentation method on the base component. Finally, the entries of a colorization matrix are created using the segmentation results. To improve compression efficiency on this framework, we construct a colorization matrix based on a correlation of base-color components. Furthermore, we embed an edge-preserving smoothing filtering process into the colorization matrix to reduce artifacts. To achieve more improvement, our method uses adaptive color conversion instead of RGB-YCbCr color conversion. Our proposed color conversion maximizes the sum of the local variance of a base component, which resulted in increment of the difference of intensities at region boundaries. Since segmentation methods partition images based on the difference, our adaptive color conversion leads to better segmentation results. Experiments showed that our method has higher compression efficiency compared with the conventional method.

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Iterative Sparse Coding for Colorization Based Compression

Lee

Kang

2014

Lecture Notes in Computer Science

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Colorization-based coding by focusing on characteristics of colorization bases

Cited by 25 publications

References 4 publications

Fast colorization based image coding algorithm using multiple resolution images

Fast colorization based image coding algorithm using multiple resolution images

Improvement of Colorization-Based Coding Using Optimization by Novel Colorization Matrix Construction and Adaptive Color Conversion

Iterative Sparse Coding for Colorization Based Compression

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