Pixel Size Reduction Loss-Less Image Compression Algorithm

Shantagiri, Pralhadrao V.; Saravanan, K.

doi:10.5121/ijcsit.2013.5208

Cited by 7 publications

(5 citation statements)

References 12 publications

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“…Several efforts [12][13][14][15][16][17][18] apply to the variety of image conversion or pixel dissimilarity or straightforward entropy coding. The input image was first divided into non overlapped blocks based on the predefined threshold values.…”

Section: Literature Reviewmentioning

confidence: 99%

Empirical Evaluation of Coding and Inter Pixel Redundancy in still Image Compression

2024

ICST Transactions on Scalable Information Systems

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The main aim of this research work is to compress grayscale images efficiently using prediction and intensity-based image compression algorithms. Image compression is useful for removing the duplication in an image to store and transmit the data in an efficient form. This research work analyzes four new schemes for gray scale lossy image compression. Among the four schemes considered, two compressive approaches are designed for Prediction Based Image Compression (PBIC) level implementation. Third approach is designed for Intensity Based Image Compression (IBIC). Finally, the previously designed PBIC and IBIC schemes lead to an Integrated Encoder. All the considered method performances are analyzed using the performance metrics. These results are compared with JPEG 2000 which is a extensively used benchmark compression encoder. The outcome of all the proposed methods is also compared with modern encoders.

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Section: Literature Reviewmentioning

confidence: 99%

Empirical Evaluation of Coding and Inter Pixel Redundancy in still Image Compression

2024

ICST Transactions on Scalable Information Systems

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“…The main limitation of this method it is used only with JPEG images compression process. Pralhadrao, V. and Saravanan, N. [14] in [2013] introduced one of the spatial domain lossless image compression algorithms called Pixel Size Reduction (PSR) for synthetic and other color images of 24 bits. The work idea is simulated to Huffman method, where image pixels are representing in least number of binary bits instead of 8 bits per color.…”

Section: Introductionmentioning

confidence: 99%

A Pixel Based Method for Image Compression

Hussain¹,

Al-Khafaji²

2022

Tikrit J. Pure Sci.

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The basic solution to overcome difficult issues related to huge size of digital images is to recruited image compression techniques to reduce images size for efficient storage and fast transmission. In this paper, a new scheme of pixel base technique is proposed for grayscale image compression that implicitly utilize hybrid techniques of spatial modelling base technique of minimum residual along with transformed technique of Discrete Wavelet Transform (DWT) that also impels mixed between lossless and lossy techniques to ensure highly performance in terms of compression ratio and quality. The proposed technique has been applied on a set of standard test images and the results obtained are significantly encourage compared with Joint Photographic Experts Group (JPEG).

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“…However, the proper selection of the mother wavelet, decomposition level and the frequency band which contains the necessary information are all potential problems that can be encountered when applying the WT [7][8]. Knowing those shortcomings, many researchers such as Shantagiri et al [9], among others, were interested in developing effective techniques for image compression. The authors proposed a new spatial domain of lossless image compression algorithm for a synthetic color image of 24 bits.…”

Section: Introductionmentioning

confidence: 99%

Image compression of surface defects of the hot-rolled steel strip using Principal Component Analysis

Boudiaf

Boubendira

Harrar

et al. 2019

Matériaux & Techniques

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The quality control of steel products by human vision remains tedious, fatiguing, somewhat fast, rather robust, sketchy, dangerous or impossible. For these reasons, the use of the artificial vision in the world of quality control has become more than necessary. However, these images are often large in terms of quantity and size, which becomes a problem in quality control centers, where engineers are unable to store these images. For this, efficient compression techniques are necessary for archiving and transmitting the images. The reduction in file size allows more images to be stored in a disk or memory space. The present paper proposes an effective technique for redundancy extraction using the Principal Component Analysis (PCA) approach. Furthermore, it aims to study the effects of the number of eigenvectors employed in the PCA compression technique on the quality of the compressed image. The results revealed that using only 25% of the eigenvectors provide very similar compressed images compared to the original ones, in terms of quality. These images are characterized by high compression ratios and a small storage space.

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Pixel Size Reduction Loss-Less Image Compression Algorithm

Abstract: ABSTRACT

Cited by 7 publications

References 12 publications

Empirical Evaluation of Coding and Inter Pixel Redundancy in still Image Compression

Empirical Evaluation of Coding and Inter Pixel Redundancy in still Image Compression

A Pixel Based Method for Image Compression

Image compression of surface defects of the hot-rolled steel strip using Principal Component Analysis

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