A novel image compression model by adaptive vector quantization: modified rider optimization algorithm

In recent days over the internet, the uploading of enormous new images is being made every day, and they necessitate large storage to accumulate the image data. For the earlier few decades, more analysts have evolved skillful image compression schemes to enhance the compression rates and the image q...

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Vydané v:	Sadhana (Bangalore) Ročník 45; číslo 1
Hlavní autori:	Chavan, Pratibha Pramod, Rani, B Sheela, Murugan, M, Chavan, Pramod
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New Delhi Springer India 01.12.2020 Springer Nature B.V
Predmet:	Adaptive algorithms Coders Compression ratio Engineering Image compression Image enhancement Image quality Mapping Optimization Optimization algorithms Vector quantization vector quantization Linde–Buzo–Gray rider optimization algorithm Image compression codebook fitness
ISSN:	0256-2499, 0973-7677
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Abstract	In recent days over the internet, the uploading of enormous new images is being made every day, and they necessitate large storage to accumulate the image data. For the earlier few decades, more analysts have evolved skillful image compression schemes to enhance the compression rates and the image quality. In this work, Vector Quantization is used, which uses the Linde–Buzo–Gray algorithm . As a novel intention, the codebooks are optimized by an improved optimization algorithm. In this approach, the database image is firstly separated into a set of blocks, i.e., pixels, and these sets of blocks are referred to as vectors. Then a suitable codeword is selected for each vector such that is the closest representation of that input vector. The encoder generates a codebook by mapping the vectors on the basis of these code words, and the compression of the vectors takes place. The encoder then sends a compressed stream of these vectors by pointing out their indices from the codebook to the decoder through a channel. The decoder then decodes the index to find out the compressed vector and places it on the image. For attaining a better image compression effect, the codebook is optimized using the Best Fitness Updated Rider Optimization Algorithm. The optimization of codebooks is done so that the summation of the compression ratio and the error difference between the original and decompressed images has to be minimized. Moreover, the proposed model is scruntized with other existing algorithms, and the experimental outcomes are validated.
AbstractList	In recent days over the internet, the uploading of enormous new images is being made every day, and they necessitate large storage to accumulate the image data. For the earlier few decades, more analysts have evolved skillful image compression schemes to enhance the compression rates and the image quality. In this work, Vector Quantization is used, which uses the Linde–Buzo–Gray algorithm. As a novel intention, the codebooks are optimized by an improved optimization algorithm. In this approach, the database image is firstly separated into a set of blocks, i.e., pixels, and these sets of blocks are referred to as vectors. Then a suitable codeword is selected for each vector such that is the closest representation of that input vector. The encoder generates a codebook by mapping the vectors on the basis of these code words, and the compression of the vectors takes place. The encoder then sends a compressed stream of these vectors by pointing out their indices from the codebook to the decoder through a channel. The decoder then decodes the index to find out the compressed vector and places it on the image. For attaining a better image compression effect, the codebook is optimized using the Best Fitness Updated Rider Optimization Algorithm. The optimization of codebooks is done so that the summation of the compression ratio and the error difference between the original and decompressed images has to be minimized. Moreover, the proposed model is scruntized with other existing algorithms, and the experimental outcomes are validated. In recent days over the internet, the uploading of enormous new images is being made every day, and they necessitate large storage to accumulate the image data. For the earlier few decades, more analysts have evolved skillful image compression schemes to enhance the compression rates and the image quality. In this work, Vector Quantization is used, which uses the Linde–Buzo–Gray algorithm . As a novel intention, the codebooks are optimized by an improved optimization algorithm. In this approach, the database image is firstly separated into a set of blocks, i.e., pixels, and these sets of blocks are referred to as vectors. Then a suitable codeword is selected for each vector such that is the closest representation of that input vector. The encoder generates a codebook by mapping the vectors on the basis of these code words, and the compression of the vectors takes place. The encoder then sends a compressed stream of these vectors by pointing out their indices from the codebook to the decoder through a channel. The decoder then decodes the index to find out the compressed vector and places it on the image. For attaining a better image compression effect, the codebook is optimized using the Best Fitness Updated Rider Optimization Algorithm. The optimization of codebooks is done so that the summation of the compression ratio and the error difference between the original and decompressed images has to be minimized. Moreover, the proposed model is scruntized with other existing algorithms, and the experimental outcomes are validated.
ArticleNumber	232
Author	Chavan, Pramod Rani, B Sheela Murugan, M Chavan, Pratibha Pramod
Author_xml	– sequence: 1 givenname: Pratibha Pramod surname: Chavan fullname: Chavan, Pratibha Pramod organization: Sathyabama Institute of Science and Technology (Deemed to be University), Department of E&TC Engineering, Trinity College of Engineering and Research – sequence: 2 givenname: B Sheela surname: Rani fullname: Rani, B Sheela email: sheelarani@sathyabama.ac.in organization: Sathyabama Institute of Science and Technology (Deemed to be University) – sequence: 3 givenname: M surname: Murugan fullname: Murugan, M organization: ECE Department, SRM Valliammai Engineering College – sequence: 4 givenname: Pramod surname: Chavan fullname: Chavan, Pramod organization: Department of E&TC Engineering, K J College of Engineering and Management Research
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Snippet	In recent days over the internet, the uploading of enormous new images is being made every day, and they necessitate large storage to accumulate the image...
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SubjectTerms	Adaptive algorithms Coders Compression ratio Engineering Image compression Image enhancement Image quality Mapping Optimization Optimization algorithms Vector quantization
Title	A novel image compression model by adaptive vector quantization: modified rider optimization algorithm
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