%0 Journal Article
%T Image Quality Assessment for Different Wavelet Compression Techniques in a Visual Communication Framework
%A Nuha A. S. Alwan
%A Zahir M. Hussain
%J Modelling and Simulation in Engineering
%D 2013
%I Hindawi Publishing Corporation
%R 10.1155/2013/818696
%X Images with subband coding and threshold wavelet compression are transmitted over a Rayleigh communication channel with additive white Gaussian noise (AWGN), after quantization and 16-QAM modulation. A comparison is made between these two types of compression using both mean square error (MSE) and structural similarity (SSIM) image quality assessment (IQA) criteria applied to the reconstructed image at the receiver. The two methods yielded comparable SSIM but different MSE measures. In this work, we justify our results which support previous findings in the literature that the MSE between two images is not indicative of structural similarity or the visibility of errors. It is found that it is difficult to reduce the pointwise errors in subband-compressed images (higher MSE). However, the compressed images provide comparable SSIM or perceived quality for both types of compression provided that the retained energy after compression is the same. 1. Introduction Images require much storage space and large transmission bandwidths. Therefore, only the essential information in an image needs to be stored, thereby giving rise to the need of image compression techniques. Naturally, the amount of this essential information is dictated by the image reconstructability. An image is dealt with as a matrix of pixel (picture element) values that are intensity dependent. Image redundancy is exploited in the compression process. Image redundancy is manifest in areas where adjacent pixels have about the same values and compression is achieved by removing this redundancy. Effective compression of image data is based on the discrete cosine transform (DCT) and the discrete wavelet transform (DWT). This transform-based compression transforms the two-dimensional data or image from the spatial domain to the frequency domain and then keeps only the useful information by removing redundancies. The human visual system (HVS) is more sensitive to energy with low spatial frequencies than to that with high spatial frequencies. Although DCT compression is less expensive, the DWT offers adaptive spatial-frequency resolution that is well suited to the properties of the HVS [1]. Digital images are prone to distortion during compression and transmission resulting in quality degradation. The most widely used IQA metric is the MSE which is computed by averaging the squared intensity differences of the distorted and reference images. MSE is simple to compute but does not always relate to perceived visual quality [2, 3]. In [2], a measure of structural similarity (SSIM) has been developed. The
%U http://www.hindawi.com/journals/mse/2013/818696/