An advanced image compression technique by using a coupled compression algorithms depend on different wavelet methods

Abstract

Digital images need a large storage capacity, and as a result they need a large bandwidth for data transmission to deliver to the desired destination over the network. Image compression technologies not only reduce the size of stored data, but also maintain as much as possible the output image quality. In the proposed research we review a technique for image compression that uses a distinct two-stage image encoding method using different compression algorithms and wavelet transform methods, which combines two types of effective compression algorithms that give more ability to compress image data.
The proposed compression technique which coupled two image compression algorithms that put to use premium characteristics from each algorithm. The wavelet transform methods contribute effectively to finding suitable solutions to supply better compression ratios for images with high resolution. The complete series of compression includes repeated stages of encoding and decoding, in addition to the wavelet processing itself.
This study will have carried out an advanced compression technique that contain a coupled compression algorithms relying on the preferred wavelets to this work from practical experiments they are, biorthogonal and Haar wavelet transform, the performance metrics for tested true HD color image will be studied. The challenge for image compression algorithms is to detect a best solution between a low compression ratio and good visual perception results. An essential measure of achieved image compression process is taken by compression ratio CR and the ratio of bit-per-pixel BPP. The CR and BPP metrics are important components in image compression techniques.
Through the results of the image compression metrics in two stages, the best practical results were obtained when the compression ratio metric CR was equal to 2.3%, and this metric indicates that the compressed image can be stored using 2.3% of the original image data size. While the BPP which represent the bit number that used to store one pixel of true color image is equal to 0.575.