Image compression using projection vector quantization with quadtree decomposition

Jung, Kyeong-Hoon; Lee, Choong Woong

doi:10.1016/0923-5965(95)00059-3

Cited by 4 publications

(3 citation statements)

References 11 publications

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“…Projection VQ (PVQ) was presented based on the adoption of quadtree decomposition to segment the images into different block sizes. This technique was defined by Single Orientation Reconstruction (SOR) and enhanced results were produced in terms of objective as well subjective ways in comparison with usage of predefined block sizes [4]. Object-relied VQ is carried out in three levels namely, initialization, iteration and finalization.…”

Section: Introductionmentioning

confidence: 99%

An Optimal Lempel Ziv Markov Based Microarray Image Compression Algorithm

Sowmyalakshmi¹,

Waly²,

Sikkandar³

et al. 2021

Computers, Materials &Amp; Continua

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In the recent years, microarray technology gained attention for concurrent monitoring of numerous microarray images. It remains a major challenge to process, store and transmit such huge volumes of microarray images. So, image compression techniques are used in the reduction of number of bits so that it can be stored and the images can be shared easily. Various techniques have been proposed in the past with applications in different domains. The current research paper presents a novel image compression technique i.e., optimized Linde-Buzo-Gray (OLBG) with Lempel Ziv Markov Algorithm (LZMA) coding technique called OLBG-LZMA for compressing microarray images without any loss of quality. LBG model is generally used in designing a local optimal codebook for image compression. Codebook construction is treated as an optimization issue and can be resolved with the help of Grey Wolf Optimization (GWO) algorithm. Once the codebook is constructed by LBG-GWO algorithm, LZMA is employed for the compression of index table and raise its compression efficiency additionally. Experiments were performed on high resolution Tissue Microarray (TMA) image dataset of 50 prostate tissue samples collected from prostate cancer patients. The compression performance of the proposed coding esd compared with recently proposed techniques. The simulation results infer that OLBG-LZMA coding achieved a significant compression performance compared to other techniques.

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Section: Introductionmentioning

confidence: 99%

An Optimal Lempel Ziv Markov Based Microarray Image Compression Algorithm

Sowmyalakshmi¹,

Waly²,

Sikkandar³

et al. 2021

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

show abstract

“…This image decomposition and reconstruction procedure is useful and important for image analysis applications. Some algorithms have been proposed as tools for image decomposition and reconstruction, such as wavelet and the related transforms [5][6][7][8], Laplacian and the related pyramid methods [9,10], principal component analysis (PCA) and independent component analysis [11,12], quadtree decomposition [13], multifilter bank [14,15], singular value decomposition [16], multiscale theory [1,2,17], and so on.…”

Section: Introductionmentioning

confidence: 99%

“…This may result in information lose. Quadtree decomposition decomposes an image into subimages with different sizes, thus the local features in some of the subimages may be useful for different applications [13]. Quadtree decomposition has been used as a tool to extract the local features in an image.…”

Section: Introductionmentioning

confidence: 99%

Image analysis through feature extraction by using top-hat transform-based morphological contrast operator

Bai

2013

Appl. Opt.

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Image decomposition and reconstruction is an important way for image analysis. To be effective for image decomposition and reconstruction, a method using extracted features through top-hat transform-based morphological contrast operator (MCOTH) is proposed in this paper. First, the morphological contrast operator constructed using the top-hat transforms is discussed. Then, extracting the bright and dark image features in the result of MCOTH is given. Based on the extracted bright and dark image features, the original images are decomposed into multiscale complete decompositions using multiscale structuring elements. After processing the decomposed images following different application purposes, the final result image can be reconstructed from the processed decomposition images. To verify the effectiveness of the proposed image analysis method through image decomposition and reconstruction, the application of image enhancement and fusion are discussed. The experimental results show that because the proposed image decomposition and reconstruction method reasonably decomposes the original image into complete decomposition with useful image features at different scales, the useful image features could be easily used for different applications. After the useful image features are processed, the final result image could be reconstructed. Moreover, different types of images are used in the experiments of image enhancement and fusion, and the results are effective. Therefore, the proposed image decomposition and reconstruction method in this paper are effective methods for image analysis and could be widely used in different applications.

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