الفهرس 
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Abstract
Image compression is in high demand as it reduces the computational time and consequently the cost in image storage and transmission. The basis for image compression is to remove redundant and unimportant data while to keep the compressed image quality in an acceptable range The present work is concerned with the performance of lossy image compression techniques. The performance of a number of algorithms is studied and compared to each other. The algorithms are belonging to three categories: transform domain, spatial domain and subband coding. Nine lossy image compression techniques using FFT, JPEG Block Truncation Coding (BTC), Absolute Moment Block Truncation Coding (AMBTC), Efficient Block Truncation Coding (EBTC), Discrete Wavelet Transform (DWT), Modified DWT, Hybrid DWT- DCT and an improved Hybrid DWT- DCT were implemented and their performance was investigated and evaluated. The performance of the selected techniques was evaluated using the six parameters: Maximum Difference (MD), Normalized Mean Squared Error (NMSE), Peak Signal-to-Noise Ratio (PSNR), Weighted Peak Signal to Noise Ratio (WPSNR), Structural Similarity Index (SSIM) and Correlation Coefficient (CF). Compression ratios of all image compression using different techniques is adjusted to be equal in order to obtain a reliable comparison. The experimental results have shown that the improved Hybrid DWT-DCT algorithm has the best performance as compared to the other eight algorithms at high compression ratios as it gives the highest peak signal-to- noise ratio (PSNR) and the highest Structural Similarity Index (SSIM) and the smallest Normalized Mean Square Error (NMSE). It gives a compression ratio of 20:1, Maximum Difference (MD) =103.8, Normalized mean square error (NMSE) = 0.0101167, peak signal-to- noise ratio (PSNR) = 26.6, Weighted Peak Signal to Noise Ratio (WPSNR) = 32.9, Structural Similarity Index (SSIM) = 0.72 and Correlation Coefficient (CF) = 0.9. As for low compression ratios, the JPEG technique performs the best.