A Quad-Tree Decomposition Approach to Cartoon Image Compression

Tsai, Yi-Chen; Lee, M.-S.; Shen, Mei-Yin; Kuo, C.-C. Jay

doi:10.1109/mmsp.2006.285350

Cited by 11 publications

(5 citation statements)

References 3 publications

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“…The results of the proposed 4C algorithm on a set of representative images, shown in Appendix A, are presented in this Section. The features of the algorithm are compared with the PNG [3], RS-LZ [9], WebP [12], SPIHT [13], the Quad-tree approach [20], as well as with the image compression standards JPEG [23] and JPEG2000 [17]. The compression efficiency and the time needed for compression and decompression were considered.…”

Section: Resultsmentioning

confidence: 99%

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An efficient lossy cartoon image compression method

Jeromel

Žalik

2019

Multimed Tools Appl

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This paper introduces a new lossy approach for compression of cartoon images. The image is firstly partitioned into regions of roughly the same colour. The chain codes are then determined of all regions. The sequence of the obtained chain code symbols is transformed with the Burrows-Wheeler Transform, Move-To-Front transform, and compressed with Run-Length Encoding. In the final step, an arithmetic encoder may be used to compress the obtained binary stream additionally. The proposed algorithm is asymmetric, meaning that the decompression does not reverse all the steps of the compression procedure. The experimental results have shown that the described method produces considerably better compression ratios than JPEG, JPEG2000, WebP, SPIHT, PNG, and two of the algorithms specialised in compression of cartoon images: the algorithm using quad-tree, and RS-LZ algorithm. August small regions. Another lossless compression method, named RS-LZ, was developed by Li et al. [9]. The method uses the Freeman chain code in eight directions (F8) to represent the borders of the solid regions, and encodes pixels that are contained neither on the border nor inside of the solid region. Because of that, images containing a lot of small solid regions and images, where edge smoothing or JPEG-like compression has been applied before, are not compressed well. A promising research was done by Taylor in 2011 [18]. He introduced a lossy compression algorithm, which takes into account small colour differences between neighbouring pixels, and a quantization procedure for small details/noise. The algorithm owns a good compression ratio at the expense of losing some image information.A new lossy method for cartoon image compression, named Chain Code Cartoon Compression (4C), is proposed in this paper. The main novelty of this work is fulfilling the gaps in cartoon image compression by developing a new approach, which would outperform the state-of-the-art general-purpose and domain-specific algorithms. The proposed method firstly divides the image into free-form regions having similar colours. The shapes of the regions are described with chain codes, which are then transformed with Burrows-Wheeler and Move-to-Front transforms. The obtained stream of chain code symbols is then encoded with Run-Length Encoding and arithmetic coding. As the algorithm is asymmetric, the decompression is faster than the compression. For cartoon images, the proposed method produces better compression ratios than the referenced algorithms. Images compressed with the proposed method are also more visually pleasing than those compressed with JPEG, JPEG2000, WebP, or SPIHT, with approximately the same structural similarity index (SSIM). In most cases, the same SSIM of two images means that they are of the same quality. PSNR is also considered of decompressed images obtained with the mentioned algorithms.This paper contains 4 sections and an Appendix. The proposed compression algorithm and its steps are presented in Section 2. The third Section contains the results of the ...

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Section: Resultsmentioning

confidence: 99%

“…Indeed, only a few cartoon image compression techniques have been reported until now. In 2006, Tsai et al presented a quasi-lossless method for compressing cartoon images using quad-trees [20]. The method applies dithering if the image contains more than 256 colours.…”

Section: Introductionmentioning

confidence: 99%

An efficient lossy cartoon image compression method

Jeromel

Žalik

2019

Multimed Tools Appl

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“…The proposed scheme is an adaptive image compression algorithm that regulates the quality of the transmitted images according to: (a) the traffic load within the network, and (b) the level of data contained in an image frame. The proposed image transmission scheme is based on the application of the QTD [8] with a varying decomposition compression factor within a gradient adaptive approach. The system architecture of this compression is shown in Fig.4.…”

Section: Adaptive Compressionmentioning

confidence: 99%

Image Compression in Wireless sensor networks- A survey

Dawood¹,

Ahila²,

Sadasivam³

2012

IJAIS

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Wireless Sensor networks are battery powered due to which their lifetime is precisely limited. In this type of network the nodes commonly have very limited resources in terms of processing power, bandwidth and energy. Efficient coding of the multimedia content is therefore important. One possible way of achieve maximum utilization of those resource is applying compression on sensor event Usually, processing data consumes much less power than transmitting data in wireless medium, so it is effective to apply compression before transmitting data for reducing total power consumption by a sensor node. In this paper various energy efficient image compression techniques Collaborative image transmission using Sobal edge-detection, JPEG2000 image compression, Image Subtraction with Quantization of image; Adaptive Compression and Spatial Correlation-Based Image Compression are discussed.

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“…The smooth or complicated regions were determined according to the statistics information. The quad-tree decomposition [7] was used to reduce classification procedures. The proper center weight of CWM was adjusted for the corresponding region.…”

Section: Introductionmentioning

confidence: 99%

Weight Switching Tri-State Median Filter

Sun

Wang

2015

AMM

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In this study, a weight switching tri-state median filter (TSM) is proposed for removing impulse noise in both smooth and complicated regions of the dealing images. The switching scheme adopted large center weight while removing impulse noise of the complicate regions and switched to small center weight while dealing with the smooth regions. The smooth and complicated regions were classified according to the statistics information of the dealing image. The quad-tree decomposition was used to reduce classification procedures. Simulation results demonstrate that the proposed center weight switching scheme makes the TSM effective in both smooth and complicated regions of images with different noise ratio.

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A Quad-Tree Decomposition Approach to Cartoon Image Compression

Cited by 11 publications

References 3 publications

An efficient lossy cartoon image compression method

An efficient lossy cartoon image compression method

Image Compression in Wireless sensor networks- A survey

Weight Switching Tri-State Median Filter

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