Space-Frequency Quantization for Image Compression With Directionlets

Velisavljević, Vladan; Beferull-Lozano, Baltasar; Vetterli, Martin

doi:10.1109/tip.2007.899183

Cited by 59 publications

(59 citation statements)

References 34 publications

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“…Several image compression techniques such as directionlets based methods [2], and hybrid methods that integrate Discrete Wavelet Transform (DWT) and fractal coding [3] or Discrete Cosine Transform (DCT) [4] are still being investigated to achieve high compression ratios without noticeable loss in the image quality.…”

Section: )mentioning

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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Section: )mentioning

confidence: 99%

Hybrid Neural Network Predictive-Wavelet Image Compression System

et al. 2015

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“…The directionlet transform proposed by the German researcher Vladan is the multi-directional anisotropy based upon the integer lattice [10][11][12]. It adopts multidirectional anisotropy basis functions; therefore, it has more advantages in expressing the image than the average wavelet transform.…”

Section: Directionlet Transformmentioning

confidence: 99%

Compressive sensing image fusion algorithm based on directionlets

Zhou

Wang

Liu

2014

J Wireless Com Network

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This paper presented a new image fusion based on compressed sensing (CS). The method decomposes two or more original images using directionlet transform, gets the sparse matrix by the directionlet coefficient sparse representation, and fuses the sparse matrices with the coefficient absolute value maximum scheme. The compressed sample can be obtained through random observation. The fused image is recovered from the reduced samples by solving the optimization. The study demonstrates that the compressive sensing image fusion algorithm based on directionlets has a number of perceived advantages. The simulations show that the proposed algorithm has the advantages of simple structure and easy implementation and also can achieve a better fusion performance.

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“…The directionlets transform proposed by Velisavljevic et al [7][8][9] is the multi-directional anisotropy based upon the integer lattice. It adopts multi-directional anisotropy basis functions, therefore, it has more advantages in expressing the image than the average wavelet transform.…”

Section: Directionlet Transformmentioning

confidence: 99%

Infrared and visible image fusion technology based on directionlets transform

Zhou

Yin

Liu

et al. 2013

J Wireless Com Network

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The article provides an infrared and visible image fusion algorithm based on directionlets transform. The registered original images were decomposed into the low-frequency and high-frequency coefficients by directionlets transform. Getting the mean of the low-frequency coefficients, applying the local variance maximum principle to the high-frequency coefficients, thereby the fusion coefficients of the fused image can be acquired. Finally, the fused image was obtained using inverse directionlets transform. The experiment indicates that the fusion algorithm can extract the original image features better. Compared with the traditional fusion algorithms, the fusion algorithm presented in this article provides better subjective visual effect, and the standard deviation and entropy value would be somewhat increased.

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Space-Frequency Quantization for Image Compression With Directionlets

Cited by 59 publications

References 34 publications

Hybrid Neural Network Predictive-Wavelet Image Compression System

Hybrid Neural Network Predictive-Wavelet Image Compression System

Compressive sensing image fusion algorithm based on directionlets

Infrared and visible image fusion technology based on directionlets transform

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