Hybrid Neural Network Predictive-Wavelet Image Compression System

Abstract

This paper considers a novel image compression technique called Hybrid Predictive Wavelet coding. The new proposed technique combines the properties of predictive coding and discrete Wavelet coding. In contrast to JPEG2000, the image data values are pre-processed using predictive coding to remove inter-pixel redundancy. The error values, which are the difference between the original and the predicted values, are discrete wavelet coding transformed. In this case, a nonlinear neural network predictor is utilised in the predictive coding system. The simulation results indicated that the proposed technique can achieve good compressed images at high decomposition levels in comparison to JPEG2000.The authors would like to thanks the reviewers for their constructive comments which have certainly improved the quality of the paper significantly.Reviewer #1: -The paper proposes an image compression system based on neural networks. I found the paper well written, technically sound and with clear focus.-The authors have provided a good study of related techniques and the proposed approach is well motivated. I have two minor comments to improve the experimental part of the paper further.The authors would like to thanks the reviewer for his/her encouraging comment.-The first comment concerns the quantitative results. I suggest that the authors study the statistical significance of the results as compared to other approaches. Actually it will be helpful to test if the difference between the proposed approach and comparable approaches is statistically significant.To check the statistical significant between the proposed HNNPWA and JPPEG 2000 techniques, the authors performed a paired t-test based on the Absolute value of the error image. The calculated tvalues showed that the proposed technique outperform JPEG2000 with α = 5% significance level for a one-tailed test at decomposition levels 4, 5 and 6. The t-test indicated that there is no significant difference between the two image compression techniques at decomposition level 3. As it can be noted from Figure 8, at decomposition levels 1, 2 and 3, the visual quality of the reconstructed images for the proposed HPNNWA and JPEG2000 is very good and it is not easy to notice the difference between the original image and the constructed image for both systems.-The comparison is mainly based on the PSNR which is the most widely used approach. Are there other measures that can be used to emphasize further the advantages of the proposed approach.For all the experiments, the authors have added the mean absolute value of the error as another quality measure.-Finally, the conclusion could be improved further by adding more analysis and discussions that could be devoted to explaining the main problems that could be related to the application of the proposed approach.The conclusion section was expanded and the problem of the application of the proposed system was mentioned as requested by the reviewer.Reviewer #2: The quality of written presentation demonstrates a good standard ...

Review on Image and Video Compression Standards

“…Hussain et al [16] published Hybrid predictive wavelet coding, which is a novel technique for image compression, in which the features of predictive coding are combined with the properties of discrete wavelet coding. The inter-pixel redundancy is removed in the pre-processing stage using predictive coding.…”

A Proposed Approach for Image Compression based on Wavelet Transform and Neural Network

“…The basic idea of the scaled conjugate gradient algorithm is that during training the network the updating of the weight parameters is performed in the direction in which the performance function of the network decreases most rapidly: the negative of gradient and by calculating the gradient function of the quadratic cost function L with respect of the weights. The partial derivation of the loss function with respect to a weight w is given by [21], [ 26]:…”

“…This training method may assure the stability of our algorithm than the on-line method as it can avoid the creation of series of drawbacks caused by the change of the weights after each training pattern which can possibly resulting in an early stopping of training before all the patterns are presented to the network [21], [23], [26].…”

NN-adaptive predictive control for a class of discrete-time nonlinear systems with input-delay

“…In the last decades, predictive control has been implemented successfully in many industrial control, and shows good performance (e.g., see [16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33]). In [16,17], the NN predictive control problems for nonlinear systems were investigated. In [18,19], the applications of predictive control were discussed.…”