Nonlinear neural prediction in 1D DPCM for efficient image data coding

Manikopoulos, C.N.; Li, J.; Sun, Huifang

doi:10.1109/icassp.1991.151082

Cited by 2 publications

(1 citation statement)

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“…By reviewing the graph in Figure 2, it is clear that around two thirds of the pixels occurred more often than the average frequency. Manikopoulos [9] applied higher-order predictors (a special type of functional-link networks [10]), which take into account the non-linear interactions of the input terms, thus achieving efficient input-output mappings without the need of hidden layers. In this case, the output of n-th order HONN is given by:…”

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confidence: 99%

Hybrid Neural Network Predictive-Wavelet Image Compression System

et al. 2015

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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 ...

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confidence: 99%