Lossy image compression using SVD coding algorithm

Aishwarya, K.; Ramesh, Rachana; Sobarad, Preeti.M.; Singh, Vaibhav

doi:10.1109/wispnet.2016.7566363

Cited by 17 publications

(4 citation statements)

References 3 publications

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“…Despite rapid increases in mass storage density, processing speeds continues to surpass present options. Not only has the recent growth of dataintensive multimedia-based web services reaffirmed the need for more efficient signal and image encoding, but it has also elevated signal compression to a critical component of storage and communication technologies (Aishwarya et al, 2016).…”

Section: Imentioning

confidence: 99%

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A Multi-Level Enhanced Color Image Compression Algorithm using SVD & DCT

Garg

Kumar

2022

IJRITCC

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Nowadays, computer technology is mostly concerned with storage capacity and performance. Compression of digital images has become a fundamental aspect of their transmission and storage. Due to storage and bandwidth constraints, it has become necessary to compress images before to transmission and storage. Not only can image compression techniques help to reduce storage space requirements, but they also aid increase transmission bandwidth. Color images are in trend these days during communication. Most of the researchers have worked only on grayscale images. This research proposes a hybrid approach that encompasses two cutting-edge picture compression algorithms: DCT & SVD. This research involves the advantages and strength of two cutting-edge picture compression algorithms that enable us to compress the color images without additional cost in computation, space and time. Here in this research, for experimental purposes, seam carving image dataset is used. The proposed method's performance is evaluated using the performance evaluation matrices, i.e., Size after Compression, MSE, PSNR, Normalized Co-relation (NC), Percentage Space-Saving, and Compression Ratio. The proposed method performance is also correlated with the two latest image compression techniques, i.e., DCT Block Truncation (DCTBT) and Discrete Cosine Transform - Vector Quantization (DCT-VQ). The findings show that the suggested hybrid color image compression approach is superior to existing compression according to different performance metrics.

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Section: Imentioning

confidence: 99%

“…Using the SVD approach, this strategy locates the most changeable region and decreases its size. To put it another way, SVD is a data reduction approach (Aishwarya et al, 2016).…”

Section: Singular Value Decomposition (Svd)mentioning

confidence: 99%

A Multi-Level Enhanced Color Image Compression Algorithm using SVD & DCT

Garg

Kumar

2022

IJRITCC

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“…The value obtained is further exponentiated by a factor of 0.33 in order to truly mimic the human auditory system. Finally, the RASTA-PLP coefficients are computed from an all-pole model by consecutively performing Inverse Fourier Transform (IFT), linear predictive analysis [39] and cepstral analysis [40] on the output of the previous stage.…”

Section: ) Rasta-plp Based Featuresmentioning

confidence: 99%

“…More specifically, to truly understand the dependence of the algorithm on these parameters, many experiments with various permutations of population size and number of iterations have been performed while keeping the other parameters constant. The algorithm have been evaluated for iterations of [10,20,30,40] on all the datasets and it is observed that even though the accuracy increases significantly with an increase in number of iterations from 10 to 30, the accuracy increases only by a small margin while increasing the same from 30 to 40. However, the time complexity increases exponentially with a small increase in number of iterations.…”

Section: ) Parameter Tuningmentioning

confidence: 99%

Hybrid Feature Selection Method Based on Harmony Search and Naked Mole-Rat Algorithms for Spoken Language Identification From Audio Signals

et al. 2020

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This era is dominated by artificial intelligence and its various applications-one of which is Spoken Language Identification (S-LID) which has always been a challenging issue and an important research area in the domain of speech signal processing. This paper deals with SLID to be used for Human-Computer Interaction (HCI) based applications by attempting to classify various languages from three multilingual databases namely CSS10: A Collection of Single Speaker Speech Datasets for 10 Languages, VoxForge and Indian Institute of Technology, Madras (IIT-Madras) speech corpus database by extracting their Mel-Spectrogram features and Relative Spectral Transform-Perceptual Linear Prediction (RASTA-PLP) features. A new hybrid Feature Selection (FS) algorithm have been developed using the versatile Harmony Search (HS) algorithm and a new nature-inspired algorithm called Naked Mole-Rat (NMR) algorithm to select the best subset of features and reduce the model complexity to help it train faster. This selected feature set is fed to five classifiers namely Support Vector Machine (SVM), k-Nearest Neighbor (k-NN), Multi-layer Perceptron (MLP), Naïve Bayes (NB) and Random Forest (RF). The evaluation measures used in this paper are precision, recall, f1-score, classification accuracy and number of selected features. An accuracy of 99.89% on CSS10, 98.22% on VoxForge and 99.75% on IIT-Madras speech corpus databases is achieved using RF. Furthermore, the proposed algorithm is found to outperform 15 standard meta-heuristic FS algorithms.

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Fast Anti-noise Compression Storage Algorithm for Big Data Video Images

Leí

2019

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Lossy image compression using SVD coding algorithm

Cited by 17 publications

References 3 publications

A Multi-Level Enhanced Color Image Compression Algorithm using SVD & DCT

A Multi-Level Enhanced Color Image Compression Algorithm using SVD & DCT

Hybrid Feature Selection Method Based on Harmony Search and Naked Mole-Rat Algorithms for Spoken Language Identification From Audio Signals

Fast Anti-noise Compression Storage Algorithm for Big Data Video Images

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