An efficient multi balanced cuckoo search K-means technique for segmentation and compression of compound images

The images comprise not only photographic images but also graphic and text images, they are determined in magazines, brochures and websites. The segmentation and compression of compound images (for instance, computer-generated images, scanned documents and so on) are tough to the procedure.The existing segmentation and compression techniques do not provide a complete comprehensive solution. To solve the problems in existing techniques, here we segmented the compound images via an optimization depended on K-means clustering technique along with AC (Alternate Current) coefficient method for the dynamic segmentation and then compressed individually. The AC coefficient based segmentation method results in detachment of smooth (background) and non-smooth (text, image and overlapping) areas. Further, the non-smooth part is segmented via the optimization depended on K-means clustering technique. Also, the density of segmented objects is headed applying different compression strategies such as the Huffman coder, arithmetic coder, and Jpeg coders. With the being approaches, the entire projected architecture is implemented in MATLAB and the function of the scheme is measured and equated. Our proposed system achieves better compression ratio (21.16), and also improves the performance for image quality index (0.931574), PSNR (Peak Signal to Noise Ratio) (34.91338), RMSE (Root Mean Square Error) (0.931574), SSIM (Structural Similarity) (0.546882), and SDME (Second Derivative-like Measure of Enhancement) (44.91293) than the available CS K-means algorithm.