DCT-based image compression using wavelet-based algorithm with efficient deblocking filter

This work adopts DCT and modifies the SPIHT algorithm to encode DCT coefficients. The algorithm represents the DCT coefficients to concentrate signal energy and proposes combination and dictator to eliminate the correlation in the same level subband for encoding the DCT-based images. The proposed algorithm also provides the deblocking function in low bit rate in order to improve the perceptual quality. This work contribution is that the coding complexity of the proposed algorithm for DCT coefficients is just close to JPEG but the performance is higher than JPEG2000. Experimental results indicate that the proposed technique improves the quality of the reconstructed image in terms of both PSNR and the perceptual results close to JPEG2000 at the same bit rate.

1. Introduction

Transform coding decomposes signal from spatial domain to other space using a well-known transform and encode these coefficients in new domain. Transform coding has higher compression performance than predictive coding in general, but requires more computation. The transform coding is an efficient compression method and the transforms include Karhunen-Loeve transform (KLT) [1], Discrete Cosine Transform (DCT) [2], Discrete Wavelet Transform (DWT) [3], Complex Wavelet Transform (CWT)[4] etc. KLT is the most optimal block based transform for data compression in a statistical sense because it optimally decorrelates an image signal in the transform domain by packing the most information in a few coefficients and minimizes the mean square error between the reconstructed and original image compared to any other transform. The performance of DCT is very much near to the statistically optimal KLT because of its nice decorrelation and energy compaction properties. Moreover, as compared to KLT, DCT is data independent and many fast algorithms exist for its fast calculation so it is extensively used in multim edia compression standards.