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Proceedings ArticleDOI

Binary image scrambling evaluation method based on the mean square deviation and the bipartite graph

05 May 2010-Vol. 1, pp 237-239
TL;DR: This method consists of dividing the image into the same size blocks, constructing the bipartite graph of every block, calculating its degree, building up a real sequence, analyzing the discrete degree of the sequence by applying the variance and mean square deviation, and, finally, deciding the scramblingdegree of the binary image.
Abstract: Wide and even distribution of pixels is the main parameter to evaluate the scrambling degree of the binary image. An evaluation method for the scrambling degree of the binary image is put forward by quoting the concepts of the bipartite graph and its degree along with applying the characteristic which the variance and mean square deviation can measure the discrete degree of nodes according to the pixels characteristic of the binary image. This method consists of dividing the image into the same size blocks, constructing the bipartite graph of every block, calculating its degree, building up a real sequence, analyzing the discrete degree of the sequence by applying the variance and mean square deviation, and, finally, deciding the scrambling degree of the binary image.
Citations
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01 Jan 2012
TL;DR: The paper firstly analyzed the Arnold transformation process to get some universal rules about the periodicity of scrambling process, then used the improved Intersecting Cortical Model Neural Network designed especially to extract 1D signatures of the original image and scrambled images which could effectively reflect the image structure changing processing.
Abstract: Scrambling transformation plays an important role in information hiding application, so offering an effective evaluation method for scrambling algorithms is becoming increasingly necessary. The paper firstly analyzed the Arnold transformation process to get some universal rules about the periodicity of scrambling process, then used the improved Intersecting Cortical Model Neural Network (ICMNN) designed especially to extract 1D signatures of the original image and scrambled images which could effectively reflect the image structure changing processing. Finally L1 norm was adopted to evaluate the scrambling degree and the universal rules obtained above were used to verify the results. The experimental results showed that the proposed method could analyze and evaluate the scrambling degree efficiently and had a promising application future.

4 citations


Cites methods from "Binary image scrambling evaluation ..."

  • ...In [10], an image was divided into blocks, and the two-block bipartite graph and standard deviation were calculated, which could be used to evaluate the scrambling degree, but were only limited to the binary image scrambling degree evaluation....

    [...]

Journal Article
TL;DR: The results show that the calculating method of the best image scramblingdegree can well reveal the consistency of binary image scrambling degree and subjective visual effects.
Abstract: In the context of digital watermark technology,this paper introduced binary image based Arnold transform and periodicity,and discussed the calculation methods of image scrambling degree in detail so that it elaborated the best formula of image scrambling degree with the combination of pixel value variance as well as pixels and 4 differ of the gray-level.The results show that the calculating method of the best image scrambling degree can well reveal the consistency of binary image scrambling degree and subjective visual effects.

3 citations

Journal ArticleDOI
TL;DR: This work proposes a lightweight comprehensive evaluation method, which is not only lightweight but also obtain more accurate result.
Abstract: The quality of wireless user perception for cells in a particular scenario is reflected on a set of indicators. Comprehensive evaluation of those cells is the base of network optimization for operators. Traditional methods use weighted sum of all indicators as the evaluation result. However, these indicators include some ineffective ones, which leads to an unconvincing evaluation result. To achieve a convincing and accurate result, we propose a lightweight comprehensive evaluation method. Firstly, indicator selection is implemented via random forest algorithm. Secondly, those selected indicators are weighted via entropy method. Finally, we compute the score of all cells with the weights. Experiment results are given to show that the cells with higher score perform better on all indicators, which is coincide with the actual situation. Hence, our proposed method is not only lightweight but also obtain more accurate result.

1 citations


Cites methods from "Binary image scrambling evaluation ..."

  • ...MSD method reflects the degree of dispersion of a data set [20]....

    [...]

  • ...method [20] often resort to the distribution of all indicator values....

    [...]

Proceedings ArticleDOI
25 May 2020
TL;DR: This work proposes a lightweight comprehensive evaluation method for wireless user perception for cellular cells that is not only lightweight but also obtain a more accurate result.
Abstract: Comprehensive evaluation methods of wireless user perception for cellular cells in the same scenario involve multi-indicators. Traditional methods use the weighted sum of all indicators as the evaluation result. However, many unimportant indicators occupy a part of the overall weight, which leads to an unconvincing evaluation result. To achieve a convincing and accurate result, we propose a lightweight comprehensive evaluation method. Firstly, most important indicators are chosen via the random forest algorithm. Secondly, those indicators are weighted via the entropy method. Finally, we compute the score of all cells with the weights. Experiment results are given to show that the cells with higher scores perform better in all indicators, which is coincide with the actual situation. Hence, our proposed method is not only lightweight but also obtain a more accurate result.

1 citations


Cites methods from "Binary image scrambling evaluation ..."

  • ..., deviation evaluation method [8] and mean square deviation evaluation method [9], often resort to manually adjust weights for all indicators and then calculate the final score of each cell....

    [...]

Journal ArticleDOI
TL;DR: Results show that combining image bitmap decomposition with ICMNN can effectively evaluate image scrambling degree and describe the change of the structure information, which agrees with human visual perception.
Abstract: A bio-inspired visual neural network named ICMNN was adopted to extract image structural information for image scrambling evaluation. In order to describe image structure more effectively with ICMNN, image bitmap was introduced into ICMNN input field. First, the original image was decomposed into eight binary images and each was scrambled with Arnold transformation without loss of generality. Then, ICMNN was adopted to extract the structural feature sequence of bitmap images and their corresponding scrambled ones. Last, L1 norm of the structure change sequence between them was calculated to evaluate the scrambling degree of the scrambling images. Results show that combining image bitmap decomposition with ICMNN can effectively evaluate image scrambling degree and describe the change of the structure information, which agrees with human visual perception. This evaluation algorithm is also independent of the scrambling algorithm and has a good versatility.

1 citations


Cites background from "Binary image scrambling evaluation ..."

  • ...In [10], it combines mean square signal-to-noise ratio and optimal blocks applied in color images which can correctly evaluate the image scrambling degree to a certain extent, but lacks the consideration of the global information of images so that some image scrambling evaluation is not conformity with visual effect....

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References
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Journal Article
TL;DR: Various watermarking algorithms for binary images are reviewed, and their advantages and disadvantages are analyzed.
Abstract: In the era of computer network, it is essential to take measures for copyright protection and data authentication of binary images, whose transmission is a major activity during everyday communication Digital watermarking techniques have been proposed to address the issue This paper reviews various watermarking algorithms for binary images, and analyzes their advantages and disadvantages The paper also proposes several research topics at next stage

15 citations

Journal Article
TL;DR: Various watermarking algorithms for binary images are reviewed, and their advantages and disadvantages are analyzed.
Abstract: In the era of computer network, it is essential to take measures for copyright protection and data authentication of binary images, whose transmission is a major activity during everyday communication. Digital watermarking techniques have been proposed to address the issue. This paper reviews various watermarking algorithms for binary images, and analyzes their advantages and disadvantages. The paper also proposes several research topics at next stage.

9 citations

Journal Article
TL;DR: Experiment demonstrates that this scheme sufficiently increases hidden information capacity more than percent 100 compared with the general hiding information algorithm.
Abstract: Based on the characteristics of binary image whose value is 0 or 1 and in which just less information can be hidden,A large capacity hiding information scheme is proposed.In this scheme we divides the binary image into blocks of size 2×2,and then determine adaptively the bit number which can be embedded in it in order to reduce the negative impact to it according to the difference of black pixel number and white pixel number.Extracting hidden information is absolutely blind Extracting without the original host image and other information.Experiment demonstrates that this scheme sufficiently increases hidden information capacity more than percent 100 compared with the general hiding information algorithm.

4 citations


"Binary image scrambling evaluation ..." refers methods in this paper

  • ...An evaluation method for the scrambling degree of the binary image is put fOl'Ward by quoting the concepts of the bipartite graph and its degree along with applying the characteristic which the variance and mean square deviation can measure the discrete degree of nodes according to the pixels…...

    [...]

Journal Article
TL;DR: The results show that the calculating method of the best image scramblingdegree can well reveal the consistency of binary image scrambling degree and subjective visual effects.
Abstract: In the context of digital watermark technology,this paper introduced binary image based Arnold transform and periodicity,and discussed the calculation methods of image scrambling degree in detail so that it elaborated the best formula of image scrambling degree with the combination of pixel value variance as well as pixels and 4 differ of the gray-level.The results show that the calculating method of the best image scrambling degree can well reveal the consistency of binary image scrambling degree and subjective visual effects.

3 citations