A New Approach Based on Order Reduction Using Sub Image Formation in Minimizing the Computation Time for Image Compression
| Autor: | Mahaboob Basha, B. C. Jinaga |
|---|---|
| Rok vydání: | 2015 |
| Předmět: |
Lossless compression
Texture compression Computer science business.industry Image quality ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Data compression ratio Data_CODINGANDINFORMATIONTHEORY Lossy compression Signal Processing Computer vision Artificial intelligence business Feature detection (computer vision) Data compression Image compression |
| Zdroj: | International Journal of Signal Processing, Image Processing and Pattern Recognition. 8:337-346 |
| ISSN: | 2005-4254 |
| DOI: | 10.14257/ijsip.2015.8.3.31 |
| Popis: | Image compression is the process to remove the redundant information from the image so that only essential information can be stored to reduce the storage size and transmission time. Advancement in still image compression becomes essential for various applications like medical imaging and multimedia applications. The task of image compression generally refers to deriving a near approximated compressed image for the given input image. Methods for tackling this problem have to do a delicate balancing act of suppressing the unwanted effects in visualizing without losing features of interest. Different techniques were already proposed in this area which is not sufficient to maintain good quality of the compressed image without sacrificing the compression ratio. Thus, new image compression algorithm based on order reduction using sub-image formation is proposed for lossy compression scheme. Obviously, the tradeoff between compression ratio and picture quality is an important issue in image compression. The importance of running time of compression was further investigated as this process can be used in high speed data transmission applications. The simulation process is carried out determining the computation time using MATLAB. The algorithm tested for still images of different size too. |
| Databáze: | OpenAIRE |
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