DWT Based-Approach for Color Image Compression Using Genetic Algorithm

Abstract

This paper describes a color image compression technique based on Discrete Wavelet Transform (DWT) and Genetic Algorithm (GA). High degree of correlation between the RGB planes of a color image is reduced by transforming them to more suitable space by using the GA. This GA would enable us to find T1T2T3 representation, in which T1 energy is more maximized than that of T2 and T3. The result of the proposed method is compared with previous similar published methods and the former is found superior in terms of quality of the reconstructed image. Further, proposed method is efficient in compression ability and fast in implementation.

Saliency enabled compression in JPEG framework

“…The value of PSNR (in dB) shown in Table 3 for proposed method, and JPEG baseline is for the most important regions of the image and for rest of the methods for the whole image. When any DWT or DCT methods in [6, 10–12, 14] are applied on the most important regions , the mean‐square error (MSE) of these regions is higher than overall MSE. The reason for this is while applying these transform‐based methods in a region with high variance, the energy compaction is lesser compared to a region with lower variance [18], which results in higher MSE after quantisation to achieve lower bit‐rate.…”

“…On the other hand, the proposed method gives a stable curve for the most important regions as well as for the overall image. Table 3 presents the performance comparison of the proposed method with the recently published DCT-and DWT-based algorithms in [6,11,12,14] and the JPEG baseline [10] on the same test images as shown in Fig. 8.…”

“…The experimental results obtained on different colour images clearly showed that our proposed method outperforms the recently published similar methods in terms of the perceptual quality of the reconstructed images. By ranking 8 × 8 blocks, we were able to reconstruct the ROI at the decoder side more accurately than the Table 3 Performance comparison between the proposed method, JPEG baseline [10], colour block truncation code -pattern fitting (CBTC-PF) [6], colour image compression by DCT with adaptive block scanning (CDABS) [11], genetic algorithm-discrete wavelet transform (GA-DWT) [14], and difference lookup table (dLUT) [12] algorithms Image R 1 area, % JPEG [10] CBTC-PF [6] CDABS [11] GA-DWT [14] dLUT [ recent state-of-the-art works where the ROI is approximated by a rectangular bounding box. The average overhead is also found to get reduced by 36.38% by using delta encoding as a postprocessing on rank information matrix.…”

“…The methods under the second category are called transform methods [1], where the image is transformed into frequency domain. Principle component analysis [9], discrete cosine transform (DCT) [6,[10][11][12], and discrete wavelet transform (DWT) [13][14][15] are the most popular transformations used for this purpose. Such transformations concentrate the energy of the image in a few number of coefficients, making it suitable for removing perceptual redundancies.…”

Colour image compression algorithm based on the DCT transform using difference lookup table

“…Results and discussion: With aim to compare our method with other methods, we have used the same test colours images used in [5,7]. The DCT block size 16 × 16 gives the best results comparing with other block sizes 8 × 8 and 32 × 32.…”