Optimized color space for image compression based on DCT and Bat algorithm
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Optimized color space for image compression based on DCT and Bat algorithm Djamel Eddine Touil1
· Nadjiba Terki2
Received: 1 May 2020 / Revised: 7 August 2020 / Accepted: 27 August 2020 / © Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract This paper develops an efficient color image compression method based on the DCT and a new color base. Digital color images are commonly represented in RGB space. Generally, it is noted that a strong correlation exists between the three planes R, G, and B of a color image. The reduction of this correlation certainly offers an advantage in the compression of RGB images. In this context, there is an infinite number of possible spaces to represent the RGB image. The main contributions of this paper are summarized in two main points. First, we design an optimized color space B1 B2 B3 using the Bat algorithm (BA) to pass from the RGB space to space more appropriate for each image. This space is produced by maximizing the energy of the image in the plane B1 more than in B2 and B3 . Second, we produce optimized thresholds appropriate to each plane of the converted image. The Bat algorithm optimizes the cost function to compute thresholds to partially reduce the number of the less significant DCT coefficients that correspond to the lower quantity of energy. The reported results against those of recent methods prove that the proposed method presents high performances in terms of peak signal to noise ratio (PSNR) on the commonly used test color images as well as the test medical images in literature. Keywords Compression · DCT · PSNR · bpp · BA · Color base
1 Introduction Today, images are an important tool that represents and records visual observation. The fast growth of digital technology in the computer vision field has opened the way for many image processing applications such as image compression [18, 19], image description, and reconstruction [20], image watermarking [21],... etc. Image compression is one of the most incessant applications in this field, where it is a process dedicated to producing a condensed representation of an image, by reducing storage transmission requirements of the image. In Djamel Eddine Touil
[email protected] 1
Energy Systems Modeling Laboratory, University of Biskra, Biskra, Algeria
2
LESIA Laboratory, Department of Electrical Engineering, University of Biskra, Biskra, Algeria
Multimedia Tools and Applications
literature, several works have been proposed, where the main goal is to develop an efficient technique that controls the balance between reducing the original amount of information of the image to an adequate amount and preserving the quality of data by removing the redundancy of the image. In this context, image redundancy is presented as one of three types [2, 16]. – – –
Coding Redundancy: Reducing the coding redundancy is equivalent to represent the entire image by the lower bits number instead of a fixed one. Inter-pixel Redundancy: It refers to the elimination of dependencies between pixels to avoid du
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