An Optimized Fast Discrete Cosine Transform Approach with Various Iterations and Optimum Numerical Factors for Image Quality Evaluation

• Published in: 2011 International Conference on Computational Intelligence and Communication Networks pp. 158 - 162
• Main Authors: Nagaria, B., Hashmi, M. F., Patidar, V., Jain, N.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.10.2011
• Subjects: compression ratio (CR); CPU processing time; Discrete cosine transforms; fast discrete cosine transform (FDCT); Image coding; Image quality; Image reconstruction; iterations; mean square error (MSE); peak signal to noise ratio (PSNR); pixel frames; PSNR; Quantization; Transform coding
• ISBN: 9781457720338; 1457720337
• DOI: 10.1109/CICN.2011.31

In this paper, we have discussed the comparative study of Fast Discrete Cosine Transform (FDCT).The proposed Algorithm investigate the performance evaluation of quantization based Fast DCT and variable block size with different no of iterations based image compression Techniques. This paper has been devoted to improve image compression at low lower no of iterations and higher pixel values. The numerical analysis of such algorithms is carried out by measuring Peak Signal to Noise Ratio (PSNR), Compression Ratio (CR) and CPU processing time. In this paper we have elaborated about the compression ratio with different no iterations. We can evaluate the higher compression ratio results more effectively with lower iteration and higher pixel values than that of quality of image respectively. Image quality will be degraded at higher iteration but compression ratio is better as compare to other algorithms. Different no of iterations and quantized matrix and variable block size are chosen using FDCT for calculating MSE, PSNR and Compression Ratio for achieving the highest image quality and Compression Ratio under the same algorithm. The proposed algorithm significantly raises the PSNR and minimizes the MSE at lower iterations but as above discussion main theory is that Compression Ratio increases at higher iterations and quality of image will not be maintained at higher iterations. We have also calculated the CPU processing time for processing of image compression to find the complexity of algorithm. We have Tested this algorithm two test Images fruit with 512 × 512 pixel frame and Lena image with 256 × 256 pixel frames. Thus, we can also conclude that at the same compression ratio the difference between original and decompressed image goes on decreasing, as there is increase in image resolution.