Haar Wavelet Based Approach for Image Compression and Quality Assessment of Compressed Image

TL;DR

This paper presents a low-complexity 2D Haar wavelet-based image compression method and evaluates the quality of compressed images using various metrics, demonstrating effective data reduction and quality assessment.

Contribution

It introduces a Haar wavelet-based approach for image compression and proposes a method to evaluate compressed image quality using multiple metrics.

Findings

Achieved effective image compression with Haar wavelets.

Evaluated image quality using CR, PSNR, MOS, PQS.

Demonstrated the method's potential for efficient digital data handling.

Abstract

With the increasing growth of technology and the entrance into the digital age, we have to handle a vast amount of information every time which often presents difficulties. So, the digital information must be stored and retrieved in an efficient and effective manner, in order for it to be put to practical use. Wavelets provide a mathematical way of encoding information in such a way that it is layered according to level of detail. This layering facilitates approximations at various intermediate stages. These approximations can be stored using a lot less space than the original data. Here a low complex 2D image compression method using wavelets as the basis functions and the approach to measure the quality of the compressed image are presented. The particular wavelet chosen and used here is the simplest wavelet form namely the Haar Wavelet. The 2D discret wavelet transform (DWT) has been applied and the detail matrices from the information matrix of the image have been estimated. The reconstructed image is synthesized using the estimated detail matrices and information matrix provided by the Wavelet transform. The quality of the compressed images has been evaluated using some factors like Compression Ratio (CR), Peak Signal to Noise Ratio (PSNR), Mean Opinion Score (MOS), Picture Quality Scale (PQS) etc.