Hybrid Soft Computing for CBIR System by Integration of Edge Detection and Compression Mechanism

Demand of CBIR is growing day by day, thus there are several soft computing techniques that are used during image processing. Edge detection and compression techniques are capable to reduce the processing time of CBIR operations. But, the issue with compression mechanism is quality reduction. The goal of current investigation is to maintain picture quality after compression has been applied. The impact of processing on final picture quality is a major topic of study in field of image analysis. In addition, there is a need to explore lossless compression methods that preserve picture quality. Present study is examining distinct approaches utilized by current research article along with its operating mechanism, pros and drawback. The goal of previous studies were to improve image processing speed, but image quality was ignored. There remains need to maintain image quality throughout CBIR processes. Thus integration of edge detection and compression technique has been made to introduce hybrid soft computing approach in order to improve efficiency of CBIR system. Canny-based edge detection has been used with loss less image compression mechanism. Huffman is the lossless compression mechanism that is used to maintain the quality of image after compression. Integration of these compression and edge detection mechanism has been made with DWT in proposed study, thus hybrid approach has been used to resolve the issue of performance of conventional CBIR systems. The proposed approach prioritizes both picture quality and compression. In order to measure the improvement in picture quality due to the elimination of noise, PSNR has been implemented. The suggested work is superior to the status quo since it takes into account both quality and performance.