Parallel image compression on a workstation-cluster is discussed. We introduce a parallel meta-algorithm for image compression which is well suited for every block based image compression scheme. This algorithm is explained in more detail in the case of fractal image compression. We present experimental results of a host/node implementation on a FDDI interconnected workstationcluster using PVM.

Fractal image coding has the advantage of higher compression ratio, but is a lossy compression scheme. The encoding procedure consists of dividing the image into range blocks and domain blocks and then it takes a range block and matches it with the domain block. The image is encoded by partitioning the domain block and using Affine transformation to achieve fractal compression. The image is rec...

Image compression helps in storing the transmitted data in proficient way by decreasing its redundancy. This technique helps in transferring more digital or multimedia data over internet as it increases the storage space. It is important to maintain the image quality even if it is compressed to certain extent. Depend upon this the image compression is classified into two categories : lossy and ...

Image compression is the application of Data compression on digital images. The discrete cosine transform (DCT) is a technique for converting a signal into elementary frequency components. It is widely used in image compression. Here we develop some simple functions to compute the DCT and to compress images. An image compression algorithm was comprehended using Matlab code, and modified to perf...

With phenomenal growth in wireless multimedia image communication, the issues need to handle are resource consumption and the quality of the image being transmitted in wireless channels. The resource consumptions indicated in most literatures are bandwidth and energy consumption. The strict constrains of wireless sensor networks (WSN) on individual sensor node’s resource brings great challenges...

This work results from a fractal image compression based on iterated transforms and machine learning modeling. In this work an improved quasi-losses fractal coding scheme is addressed to preserve the rich features of the medical image as the domain blocks and to generate the remaining part of the image from it based on fractal transformations. Machine learning based model is used for improving ...

Images require substantial storage and transmission resources, thus image compression is advantageous to reduce these requirements. The objective of this paper is to evaluate a set of wavelets for image compression. Image compression using wavelet transforms results in an improved compression ratio. Wavelet transformation is the technique that provides both spatial and frequency domain informat...

Given a color image that was quantized in some hidden color space (termed compression color space) during previous JPEG compression, we aim to estimate this unknown compression color space from the image. This knowledge is potentially useful for color image enhancement and JPEG re-compression. JPEG quantizes the discrete cosine transform (DCT) coefficients of each color plane independently duri...

Image compression is the application of Data compression on digital images. The discrete cosine transform (DCT) is a technique for converting a signal into elementary frequency components. It is widely used in image compression. Here we develop some simple functions to compute the DCT and to compress images. An image compression algorithm was comprehended using Matlab code, and modified to perf...

In this paper, the comparison between Hybrid Image Compressions methods and Fuzzy logic based image Compression is discussed. The Hybrid Comparison Method is a combination of both the DCT and DWT Image Compression method. When more than one compression technique are applied to compressed one image for high value of PSNR (peak signal to noise ratio) and CR (compression ratio) this process is cal...