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

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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.

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Abbreviations

ABC:

Artificial bee colony

ACM:

Active Contour Model

BER:

Bit Error Rate

CFA:

Color Filter Array

CMOS:

Complementary Metal Oxide Semiconductor

CS:

Cuckoo search

DCT:

Discrete Cosine Transform

DTT:

Discrete Tchebichef Transform

EZW:

Embedded Zero-tree Wavelet

FIC:

Fractal Image Compression

FSS:

Fish School Search

GIF:

Graphics Interchange Format

HV:

Horizontal-Vertical

iDTT:

integer DTT

JPEG:

Joint Photographic Experts Group

LBG:

Linde-Buzo-Gray

MAE:

Mean Absolute Error

MASE:

Mean Absolute Scaled Error

MEP:

Mean Error Percentage

MF:

Matrix Factorization

MSE:

Mean Squared Error

OCR:

Optimum Compression Ratio

PNG:

Portable Network Graphics

PSNR:

Peak Signal to Noise Ratio

QDCT:

Quantum DCT algorithm

RMSE:

Root Mean Square Error

ROI:

Region Of Interest

ROI:

Region of Interest

SERMs:

Single-row Elementary Reversible Matrices

SMAPE:

Symmetric Mean Absolute Percentage Error

SNR:

Signal-to-Noise Ratio

VQ:

Vector Quantization

WCE:

Wireless Capsule Endoscopy

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Authors and Affiliations

  1. Sathyabama Institute of Science and Technology (Deemed to be University), Jeppiaar Nagar, Rajiv Gandhi Salai, Chennai, 600 119, Tamil Nadu, India

    Pratibha Pramod Chavan & B Sheela Rani

  2. Department of E&TC Engineering, Trinity College of Engineering and Research, Pisoli, Pune, 411 048, Maharashtra, India

    Pratibha Pramod Chavan

  3. ECE Department, SRM Valliammai Engineering College, Kattankulathur, Chengalpet (Dist.), 603 203, Tamil Nadu, India

    M Murugan

  4. Department of E&TC Engineering, K J College of Engineering and Management Research, Pisoli, Pune, 411048, Maharashtra, India

    Pramod Chavan

Authors
  1. Pratibha Pramod Chavan
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  2. B Sheela Rani
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  3. M Murugan
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  4. Pramod Chavan
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Corresponding author

Correspondence to B Sheela Rani.

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Chavan, P.P., Rani, B.S., Murugan, M. et al. A novel image compression model by adaptive vector quantization: modified rider optimization algorithm. Sādhanā 45, 232 (2020). https://doi.org/10.1007/s12046-020-01436-9

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  • DOI: https://doi.org/10.1007/s12046-020-01436-9

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