• Journal of Information Processing Systems
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• v.13 no.1
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• pp.114-127
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• 2017

This paper proposes a color image coding algorithm based on shape-adaptive all phase biorthogonal transform (SA-APBT). This algorithm is implemented through four procedures: color space conversion, image segmentation, shape coding, and texture coding. Region-of-interest (ROI) and background area are obtained by image segmentation. Shape coding uses chain code. The texture coding of the ROI is prior to the background area. SA-APBT and uniform quantization are adopted in texture coding. Compared with the color image coding algorithm based on shape-adaptive discrete cosine transform (SA-DCT) at the same bit rates, experimental results on test color images reveal that the objective quality and subjective effects of the reconstructed images using the proposed algorithm are better, especially at low bit rates. Moreover, the complexity of the proposed algorithm is reduced because of uniform quantization.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.49 no.3
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• pp.30-36
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• 2012

block truncation coding(BTC) image compression is known as a simple and efficient technology for image compression algorithm. In this paper, we propose RMC-BTC algorithm(RMC : reduction method chrominace data) for color image compression. To compress chrominace data, in every BTC block, the RMC-BTC coding employs chrominace data expressed with average of chrominace data and using method of luminance data bit-map to represented chrominance data bit-map. Experimental results shows efficiency of proposed algorithm, as compared with PSNR and compression ratio of the conventional BTC method.

• Proceedings of the Korea Multimedia Society Conference
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• 2012.05a
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• pp.40-42
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• 2012

Colorization is a method using computer to add color to a black and white image automatically. The input is a grayscale image and some representative pixels (RPs). The RPs contain the color information for the image, and it indicates each region's color information. Colorization-based coding is a novel way for lossy image compression, it decodes a color image to get grayscale image and extracts RPs from the image. Because RPs decides the region's color and we also want small data size for image compression, form this viewpoint the paper proposes a way to get better and fewer RPs based on watershed segmentation. According to the segmentation result we also improve the original chrominance blending colorization method to save decode time and get better reconstruct image.

• IEIE Transactions on Smart Processing and Computing
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• v.3 no.1
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• pp.10-18
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• 2014

From the perspective of reducing the sampling cost of color images at high resolution, block-based compressive sensing (CS) has attracted considerable attention as a promising alternative to conventional Nyquist/Shannon sampling. On the other hand, for storing/transmitting applications, CS requires a very efficient way of representing the measurement data in terms of data volume. This paper addresses this problem by developing a measurement-coding method with the proposed customized Huffman coding. In addition, by noting the difference in visual importance between the luma and chroma channels, this paper proposes measurement coding in YCbCr space rather than in conventional RGB color space for better rate allocation. Furthermore, as the proper use of the image property in pursuing smoothness improves the CS recovery, this paper proposes the integration of a low pass filter to the CS recovery of color images, which is the block-based ${\ell}_{20}$-norm minimization. The proposed coding scheme shows considerable gain compared to conventional measurement coding.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.5
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• pp.2326-2345
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• 2016

For the compression of color images, a common bitmap usually is generated to replace the three individual bitmaps that originate from block truncation coding (BTC) of the R, G and B channels. However, common bitmaps generated by some traditional schemes are not the best possible because they do not consider the minimized distortion of the entire color image. In this paper, we propose a near-optimized common bitmap scheme for BTC using Binary Ant Colony Optimization (BACO), producing a BACO-BTC scheme. First, the color image is compressed by the BTC algorithm to get three individual bitmaps, and three pairs of quantization values for the R, G, and B channels. Second, a near-optimized common bitmap is generated with minimized distortion of the entire color image based on the idea of BACO. Finally, the color image is reconstructed easily by the corresponding quantization values according to the common bitmap. The experimental results confirmed that reconstructed image of the proposed scheme has better visual quality and less computational complexity than the referenced schemes.

• Journal of IKEEE
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• v.2 no.2 s.3
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• pp.185-193
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• 1998

Fractal-based monochrome image coding method can be easily applied for color image compression by splitting the color image into different primary spectral channels such as RGB, YIQ or $YC_bC_r$, and encoding each channel independently According to this method, it needs to repeat the fractal coding for each channel, so it have the problem of encoding time. In this paper, a fractal-based coder for color still image is proposed which features the enhancement of compression rate and the reduction of coding time. As the result of the experiment where the proposed algorithm is applied far color images, the compression rate is enhanced by 28 : 1 above with average PSNR value $28{\sim}29[dB]$, do not lossless encoding process using JPEG. And the encoding time is reduced by maximum 11.5 %.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 1997.06a
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• pp.19-24
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• 1997

This paper describes an image segmentation technique for the object-oriented coding at very low bit rates. By noting that, in the object-oriented coding technique, each objects are represented by 3 parameters, namely, shape, motion, and color informations, we propose a segmentation technique, in which the 3 parameters are fully exploited. To achieve this goal, starting with the color space conversion and the noise reduction, the input image is divided into many small regions by the K-menas algorithm on the O-K-S color space. Then, each regions are merged, according to the shape and motion information. In simultations, it is shown that the proposed technique segments the input image into relevant objects, according to the shape and motion as well as the colors. In addition, in order to evaluate the performance of the proposed technique, we introduce the notion of the interesting regions, and provide the results of encoding the image with emphasizing the interesting regions.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.12
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• pp.3149-3165
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• 2013

Content based image retrieval is increasingly gaining popularity among image repository systems as images are a big source of digital communication and information sharing. Identification of image content is done through feature extraction which is the key operation for a successful content based image retrieval system. In this paper content based image retrieval system has been developed by adopting a strategy of combining multiple features of shape, color and relevance feedback. Shape is served as a primary operation to identify images whereas color and relevance feedback have been used as supporting features to make the system more efficient and accurate. Shape features are estimated through second derivative, least square polynomial and shapes coding methods. Color is estimated through max-min mean of neighborhood intensities. A new technique has been introduced for relevance feedback without bothering the user.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.34S no.5
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• pp.45-51
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• 1997

To trnsmit moving image signals by using an object vased coding technique at the rate of 8kbps~11kbps, it is very important to minimize the bit rates used for the compression of the color information of MF-objects. This paper proposes a hybrid coding method which uses the shpae adaptive coding method and the interframe reference method selectively. Gilge's shape adaptive orthogonal coidng method is utilized for shpae adaptive coding. The interfarame reference method approximates the low-passed signals of the image by gilge's shpae adaptive orthogonalization method and then refers the approximation error signals from the high frequency signal components of th eprevious frame. The proposed method achives the bit rates reduction of 17% compared to the gilge's shape adaptive orthogonalization method and 30% rduction compared to the shape adaptive DCT in average.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.01a
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• pp.838-841
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• 2009

This paper proposes a novel lossless coding scheme for Bayer color filter array (CFA) images which are generally used as internal data of color digital cameras having a single image sensor. The scheme employs a block-adaptive prediction method to exploit spatial and spectral correlations in local areas containing different color signals. In order to allow adaptive prediction suitable for the respective color signals, four kinds of linear predictors which correspond to 2 ${\times}$ 2 samples of Bayer CFA are simultaneously switched block-by-block. Experimental results show that the proposed scheme outperforms other state-of-the-art lossless coding schemes in terms of coding efficiency for Bayer CFA images.