Image compression based on 2D Discrete Fourier Transform and matrix minimization algorithm

Rasheed, Mohammed H.; Salih, Omar M.; Siddeq, Mohammed M.; Rodrigues, Marcos

doi:10.1016/j.array.2020.100024

Cited by 25 publications

(7 citation statements)

References 9 publications

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“…To reduce the compression time and enhance the compression efficiency, a high computational load of the computer is required (i.e., run-length encoding, adaptive Huffman encoding, and Lempel Zec Welch algorithm), which is hard to implement for real-time compression [38] . In addition, other methods to reduce the computational load such as the discrete Fourier transform and discrete cosine transform, have limitations of reconstruction degree and fundamental differences in structural similarity and PSNR between decompressed data and original data [39] , [40] . In addition, these compression method has fundamental limitations of depth-related information loss because of MAP image-based optimal parameter selecting method.…”

Section: Discussionmentioning

confidence: 99%

Three-dimensional reconstructing undersampled photoacoustic microscopy images using deep learning

et al. 2023

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Section: Discussionmentioning

confidence: 99%

Three-dimensional reconstructing undersampled photoacoustic microscopy images using deep learning

et al. 2023

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“…PSNR is calculated by taking the proportion of the original signal's maximum possible power to the power of the distortion (difference between the actual and reconstructed signals). The high PSNR intimates the improved quality of the reconstructed signal [9]. PSNR is typically calculated as given in equation ( 3).…”

Section: Percentage Of Compression = Bits Before Compresion−bits Afte...mentioning

confidence: 99%

Survey On Parameters of Data Compression

Nithya

Vengattaraman

Sathya

2023

jdaai

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The rapid development in the hardware and the software gives rise to data growth. This data growth has numerous impacts, including the need for a larger storage capacity for storing and transmitting. Data compression is needed in today’s world because it helps to minimize the amount of storage space required to store and transmit data. Performance measures in data compression are used to evaluate the efficiency and effectiveness of data compression algorithms. In recent times, numerous data compression algorithms are developed to reduce data storage and increase transmission speed in this internet era. In order to analyses how data compression performance is measured in terms of text, image, audio, and video compressions. This survey presents discussion made for important data compression parameters according to their data types.

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“…An efficient codebook with a limited working time, and with tuneable pulse emission rate as well as bats, the optimal peak signal-to-noise relation (PSNR) is the automatic zoom-function. Rasheed et al [9] have created a new method to compress high-resolution images with a DFT and a Matrix Minimization (MM) model. It consists of modifying the picture using DFT, which produces real and imaginative elements.…”

Section: Related Workmentioning

confidence: 99%

Optimization Based Vector Quantization for Data Reduction in Multimedia Applications

Kavitha¹,

Kanchana²,

Gobinathan³

et al. 2022

Intelligent Automation &Amp; Soft Computing

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Data reduction and image compression techniques in the present Internet and multi-media age are essential to increase image and video capacity in relation to memory, network bandwidth use and safe data transmission. There have been a different variety of image compression models with varying compression efficiency and visual image quality in the literature. Vector Quantization (VQ) is a widely used image coding scheme that is designed to generate an efficient coding book that includes a list of codewords that assign the input image vector to a minimum distance of Euclidea. The Linde-Buzo-Gray (LBG) historically widely used model produces the local optimal codebook. The LBG model's codebook architecture is seen as an optimization challenge that can be resolved using metaheuristic algorithms. In this perspective, this paper introduces a new DPIO algorithm for codebook generation in VQ. The model presented uses LBG to initialise the DPIO algorithm to construct the VQ technique and is called the DPIO-LBG process. The performance of the DPIO-LBG model is validated with benchmark data and the effects of different performance aspects are investigated. The simulation values showed the DPIO-LBG model to be efficient interfaces of compression efficiency and image quality reconstructed. The presented model produces an efficient codebook with minimum computational time and a better signal to noise ratio (PSNR).

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Image compression based on 2D Discrete Fourier Transform and matrix minimization algorithm

Cited by 25 publications

References 9 publications

Three-dimensional reconstructing undersampled photoacoustic microscopy images using deep learning

Three-dimensional reconstructing undersampled photoacoustic microscopy images using deep learning

Survey On Parameters of Data Compression

Optimization Based Vector Quantization for Data Reduction in Multimedia Applications

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