Lossy image compression based on prediction error and vector quantisation


Ahamed Ayoobkhan, Mohamed Uvaze and Ramakrishnan, Kannan and Eswaran, Chikkannan (2017) Lossy image compression based on prediction error and vector quantisation. EURASIP Journal on Image and Video Processing, 35. pp. 1-13. ISSN 1687-5281

[img] Text
Restricted to Repository staff only

Download (3MB)


Lossy image compression has been gaining importance in recent years due to the enormous increase in the volume of image data employed for Internet and other applications. In a lossy compression, it is essential to ensure that the compression process does not affect the quality of the image adversely. The performance of a lossy compression algorithm is evaluated based on two conflicting parameters, namely, compression ratio and image quality which is usually measured by PSNR values. In this paper, a new lossy compression method denoted as PE-VQ method is proposed which employs prediction error and vector quantization (VQ) concepts. An optimum codebook is generated by using a combination of two algorithms, namely, artificial bee colony and genetic algorithms. The performance of the proposed PE-VQ method is evaluated in terms of compression ratio (CR) and PSNR values using three different types of databases, namely, CLEF med 2009, Corel 1 k and standard images (Lena, Barbara etc.). Experiments are conducted for different codebook sizes and for different CR values. The results show that for a given CR, the proposed PE-VQ technique yields higher PSNR value compared to the existing algorithms. It is also shown that higher PSNR values can be obtained by applying VQ on prediction errors rather than on the original image pixels.

Item Type: Article
Uncontrolled Keywords: Image compression, Artificial neural network, Prediction errors, Vector quantization, Artificial bee colony algorithm and genetic algorithm
Subjects: T Technology > TA Engineering (General). Civil engineering (General) > TA1501-1820 Applied optics. Photonics
Divisions: Faculty of Computing and Informatics (FCI)
Depositing User: Ms Suzilawati Abu Samah
Date Deposited: 22 Jul 2020 01:26
Last Modified: 22 Jul 2020 01:26
URII: http://shdl.mmu.edu.my/id/eprint/6967


Downloads per month over past year

View ItemEdit (login required)