Colored Image Compression Using Gradient Adjustment Prediction Based Wavelet

Problem statement: Uncompressed graphics, audio and video data require considerable storage capacity and transmission bandwidth. Despite rapid progress in mass-storage density, processor speeds and digital communication system performance, demand for data storage capacity and data transmission bandwidth continues to outstrip the capabilities of available technologies. The recent growth of data intensive digital audio, image and video (multimedia) based web applications, have not only sustained the need for more efficient ways to encode signals and images but have made compression of such signals central to signal-storage and digital communication technology. Approach: The objective includes developing and applying an efficient Space-Frequency Segmentation (SFS) as an image partitioning scheme, then using an appropriate entropy-coding algorithm that can be used with the developed segmentation to improve compression performance, particularly in the case of still image compression. The proposed compression system focuses on an innovative scheme for adaptive wavelet coding technique combined with spatial encoding. Result: Experiments conducted using the proposed system produced encouraging results. The entropyspatial coders used in the proposed system produced better results than those obtained by using the basic arithmetic coder. It provides more appropriate rate-distortion optimization for the spacefrequency segmentation than the basic arithmetic coder does. The proposed compression system implies some control coding parameters; the effects of these parameters were investigated to determine the suitable range for each one of them. Conclusion: We conclude that a comparison between the energy of two partitioning types (space and frequency) shows that the energy of frequency partitioning is greater than the space partitioning from the point of view of quality of compressed image. And also the selection of parameter value used in SFS part (Ratio and Threshold) should be chosen correctly, because its value has a great effect on the compressed image quality. And the selection of the quantization step size and the quantization factor (weight) should be done carefully, because experimental results show that these two factors have a great effect on the compression ratio and PSNR factor. Finally experimental results indicate that the use of GAP predictor produces better PSNR than other predictors.