A novel image compression model by adaptive vector quantization: modified rider optimization algorithm
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Sådhanå (2020)45:232 https://doi.org/10.1007/s12046-020-01436-9
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A novel image compression model by adaptive vector quantization: modified rider optimization algorithm PRATIBHA PRAMOD CHAVAN1,2, B SHEELA RANI1,*, M MURUGAN3 and PRAMOD CHAVAN4 1
Sathyabama Institute of Science and Technology (Deemed to be University), Jeppiaar Nagar, Rajiv Gandhi Salai, Chennai 600 119, Tamil Nadu, India 2 Department of E&TC Engineering, Trinity College of Engineering and Research, Pisoli, Pune 411 048, Maharashtra, India 3 ECE Department, SRM Valliammai Engineering College, Kattankulathur, Chengalpet (Dist.) 603 203, Tamil Nadu, India 4 Department of E&TC Engineering, K J College of Engineering and Management Research, Pisoli, Pune 411048, Maharashtra, India e-mail: [email protected]; [email protected]; [email protected]; [email protected] MS received 17 September 2019; revised 9 May 2020; accepted 15 May 2020 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. Keywords. Image compression; vector quantization; Linde–Buzo–Gray; codebook; rider optimization algorithm; fitness.
1. Introduction Image compression [1–3] is considered as the progression of minimizing the byte size in a graphics file in spite of weakening the quality of image to a detrimental level. The file size minimization thus authorizes more images to be accumulated around the disk or memory space in specified amount. The time needed for the transmission of image around the Internet or downloaded fro
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