No abstract

No abstract

Digital circuits are designed and implemented with respect to time and area optimization. Particularly in the field of image processing, data is to be transmitted from source to destination with optimum conditions of quality and transmitting speed. These objectives are achieved in diverse schemes under different circumstances. This paper describes a method to deal with image compression techniques to explain various aspects of image quality improvement associated with quality controlling factors. Huffman algorithm is utilized by developing pyramid of data for encoding and decoding process. This algorithm is applied on pre and post processing architecture which operates efficiently in varying system band width conditions. Moreover, results acquired by developed code enable us to understand different important parameters those affect image quality.

In this paper, an improved fractal image compression (FIC) based on peer adjacent scheme and domain classification was proposed. The proposed method has low computation cost since it contains no search operations, thus becoming fast irreversible fractal scheme. Comprehensive experiments on a standard test image and several types of digital radiology images revealed that the proposed method is competitive when compared to established quadtree-based FIC techniques. The novelty of the proposed method lies in the use of this improved domain classification and mapping strategy for accurate and more precise FIC encoding. The empirical result of standard test image suggests that the proposed method is more competitive compared to the established schemes and achieves better performance in terms the peak signal-to-noise ratio (PSNR) and compression time averaging at 27.27 dB and 6.88 s, respectively. Also, the proposed method obtains an efficient compression ratio with 16.13 compared to others. Additionally, experiments involving various medical image modalities confirmed the superiority of the proposed method for practical applications of medical image compression.