Lossless and Lossy Polynomial Image Compression

Al-Khafaji, Ghadah; Rajab, Maha A.

doi:10.9790/0661-1804025662

Cited by 3 publications

(5 citation statements)

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“…Ghadah [21] (2014): suggested a lossless hybrid compression system of multi-DWT and linear polynomial coding, where the partitioned fixed blocks are classified into either edge or non-edge blocks, where for edge blocks use mean values, while for non-edge blocks use DWT of Haar base, then for the approximation sub band use linear polynomial coding, with coding the details of sub bands losslessly. The tested performance adopted four medical grayscale images of MRI and US bases of square size 256x256, with a number of edge pixels between 2 to 10, and a compression ratio between 5 to 9 on average.…”

Section: Lossless Polynomial Codingmentioning

confidence: 99%

“…The system was tested using the same test images adopted by Ghadah & loay [1], with superior performances compared to the mentioned paper, where compression ratios were between 9 to 11, with peak signal-tonoise ratio (PSNR)values between 39 dB to 40dB. Ghadah&Haider [20] Mixed between DWT and polynomial coding along efficient entropy coding Hierarchal scheme exploits the approximation sub band with polynomial coding instead of the whole image with a compression ratio of between 7 to 10 on average 3 Ghadah [21] Classify blocks into edge and nonedge block each compressed differently Improved the compression performance using the multiwavelet scheme 4…”

Section: Lossy Polynomial Codingmentioning

confidence: 99%

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Image Compression using Polynomial Coding Techniques: A review

Mahdi,

AL-Khafaji

2022

J. Al-Qadisiyah Comp. Sci. Math.

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Compression solves the two serious issues of size and transmission efficiently, and various standard techniques are available, such as GIF, JPEG, MPEG, and MP3, in which these techniques combine efficiency, ease of use, and speed with the ability to fulfill a variety of requirements, but there's always a necessity and demand for new innovative techniques to improve the digital realm. Polynomial coding is one of the simple promising compression techniques of modeling base that composed of deterministic part (coefficients) and probabilistic part (residual) to represent image information compactly, there are many models were developed to improve the discipline of this technique performance, where This paper review focused on researchers' efforts to compress grayscale and color images using linear and non-linear lossless and lossy techniques.

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Section: Lossless Polynomial Codingmentioning

confidence: 99%

Section: Lossy Polynomial Codingmentioning

confidence: 99%

Image Compression using Polynomial Coding Techniques: A review

Mahdi,

AL-Khafaji

2022

J. Al-Qadisiyah Comp. Sci. Math.

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“…Predictive coding can be made superiorly with the help of the compression ratio. Some researchers describe prediction-based lossless compression [1][2][3][4][5]. The research work that was proposed previously for grayscale image compression is analyzed in this paper.…”

Section: Introductionmentioning

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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“…In case of the composite DWT-IBAQC method, IBAQC is applied to the DWT coefficients of the image. Since the whole energy of the image is carried by only a few wavelet (DWT) coefficients, the IBQAC is used to encode only the coarse (low pass) wavelet coefficients [12].Some researchers describe prediction-based lossless compression [1,3,[19][20][21][22][23][24]. Moreover, the combination of wavelet transform and the concept of prediction are presented in some studies [25,26].…”

mentioning

confidence: 99%

“…Some researchers describe prediction-based lossless compression [1,3,[19][20][21][22][23][24]. Moreover, the combination of wavelet transform and the concept of prediction are presented in some studies [25,26].…”

mentioning

confidence: 99%

Edge-Based and Prediction-Based Transformations for Lossless Image Compression

Kabir

Mondal

2018

J. Imaging

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Pixelated images are used to transmit data between computing devices that have cameras and screens. Significant compression of pixelated images has been achieved by an "edge-based transformation and entropy coding" (ETEC) algorithm recently proposed by the authors of this paper. The study of ETEC is extended in this paper with a comprehensive performance evaluation. Furthermore, a novel algorithm termed "prediction-based transformation and entropy coding" (PTEC) is proposed in this paper for pixelated images. In the first stage of the PTEC method, the image is divided hierarchically to predict the current pixel using neighboring pixels. In the second stage, the prediction errors are used to form two matrices, where one matrix contains the absolute error value and the other contains the polarity of the prediction error. Finally, entropy coding is applied to the generated matrices. This paper also compares the novel ETEC and PTEC schemes with the existing lossless compression techniques: "joint photographic experts group lossless" (JPEG-LS), "set partitioning in hierarchical trees" (SPIHT) and "differential pulse code modulation" (DPCM). Our results show that, for pixelated images, the new ETEC and PTEC algorithms provide better compression than other schemes. Results also show that PTEC has a lower compression ratio but better computation time than ETEC. Furthermore, when both compression ratio and computation time are taken into consideration, PTEC is more suitable than ETEC for compressing pixelated as well as non-pixelated images.compression are some of the wavelet-based compressions such as embedded zerotrees of wavelet transforms (EZW), joint photographic experts group (JPEG) and the moving picture experts group (MPEG) compression.A large number of research papers report image compression algorithms. For example, one study [13] is about discrete cosine transform (DCT)-based lossless image compression where the higher energy coefficients in each block are quantized. Next, an inverse DCT is performed only on the quantized coefficients. The resultant pixel values are in the 2-D spatial domain. The pixel values of two neighboring regions are then subtracted to obtain residual error sequence. The error sequence is encoded by an entropy coder such as Arithmetic or Huffman coding [13]. Image compression in the frequency domain using wavelets is reported in several studies [12,[14][15][16][17]]. In the method described in [14] lifting-based bi-orthogonal wavelet transform is used which produces coefficients that can be rounded without any loss of data. In the work of [18] wavelet transform limits the image energy within fewer coefficients which are encoded by "set partitioning in hierarchical trees" (SPIHT) algorithm.In [19] JPEG lossless (JPEG-LS), a prediction-based lossless scheme, is proposed for continuous tone images. In [14] embedded zero tree coding (EZW) method is proposed based on the zero tree hypothesis. The study in [12] proposes a compression algorithm based on combination of discrete wavelet transform ...

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Lossless and Lossy Polynomial Image Compression

Cited by 3 publications

References 1 publication

Image Compression using Polynomial Coding Techniques: A review

Image Compression using Polynomial Coding Techniques: A review

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

Edge-Based and Prediction-Based Transformations for Lossless Image Compression

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