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local complexity based adaptive embedding mechanism for reversible data hiding in digital images

MULTIMEDIA TOOLS AND APPLICATIONS 2019年第7期78卷 7911-7926页

作者： Cao, Fang An, Bowen Yao, Heng Tang, Zhenjun Shanghai Maritime Univ Coll Informat Engn Shanghai 201306 Peoples R China Univ Shanghai Sci & Technol Sch Opt Elect & Comp Engn Shanghai 200093 Peoples R China Guangxi Normal Univ Guangxi Key Lab Multisource Informat Min & Secur Guilin 541004 Peoples R China

In this paper, a reversible data hiding scheme for digital images with high hiding capacity is proposed. Original image is segmented into smooth and rough regions based on local complexity. In order to achieve higher hiding capacity, we embed three bits into each pixel belonging to smooth region with lower local complexity and one bit is embedded into each pixel of rough region, which can effectively exploit more redundancy during data embedding compared with conventional methods of prediction error expansion (PEE). Additionally, the pixel selection mechanism is applied to reduce the number of shifted pixels, which leads to high visual quality of stego image. Experimental results show that, our scheme can achieve better rate-distortion performance than some of state-of-the-art schemes.

关键词： Reversible data hiding Prediction error expansion local complexity Adaptive embedding Hiding capacity Image quality

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A dynamic saliency attention model based on local complexity

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DIGITAL SIGNAL PROCESSING 2012年第5期22卷 760-767页

作者： Wei, Longsheng Sang, Nong Wang, Yuehuan Zheng, Qingqing Huazhong Univ Sci & Technol Inst Pattern Recognit & Artificial Intelligence Wuhan 430074 Peoples R China China Univ Geosci Fac Mech & Elect Informat Wuhan 430074 Peoples R China

A dynamic saliency attention model based on local complexity is proposed in this paper. Low-level visual features are extracted from current and some previous frames. Every feature map is resized into some different sizes. The feature maps in same size and same feature for all the frames are used to calculate a local complexity map. All the local complexity maps are normalized and are fused into a dynamic saliency map. In the same time, a static saliency map is acquired by the current frame. Then dynamic and static saliency maps are fused into a final saliency map. Experimental results indicate that: when there is noise among the frames or there is change of illumination among the frames, our model is excellent to Marat's model and Shi's model;when the moving objects do not belong to the static salient regions, our model is better than Ban's model. (C) 2012 Elsevier Inc. All rights reserved.

关键词： Dynamic saliency model Attentional selection local complexity Saliency map

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Efficient capacity-distortion reversible data hiding based on combining multipeak embedding with local complexity

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APPLIED INTELLIGENCE 2022年第11期52卷 13006-13026页

作者： Fu, Zhifeng Gong, Mengxin Long, Guoqiang Gan, Zhihua Chai, Xiuli Lu, Yang Henan Univ Sch Artificial Intelligence Zhengzhou 450046 Peoples R China Henan Univ Henan Key Lab Big Data Anal & Proc Kaifeng 475004 Peoples R China Henan Univ Inst Intelligent Network Syst Sch Software Intelligent Data Proc Engn Res Ctr Henan Prov Kaifeng 475004 Peoples R China

Recently, most histogram shifting-based reversible data hiding (RDH) algorithms have considered the impact of textural information on embedding performance, while exploiting pixels with different local complexities. Prioritizing pixels with small local complexities to accommodate secret data decreases the invalid shifting pixels, thereby reducing distortion. However, though effective, the local complexity is not calculated precisely enough, which results in inaccurate texture division and does not considerably reduce distortion. Thus, we employ a novel local complexity calculation and multipeak embedding (MPE) to effectively improve the capacity-distortion performance. Specifically, the host image is first preprocessed by the dot-cross pattern and divided into two subsets. Then the pixel local complexity of each subset is computed by using the spatial correlation of pixels (SCOP) to improve calculation accuracy. Finally, the peak bins to be expanded in the regions with lower local complexity are adaptively selected for embedding with secret data by MPE. To ensure that the authorized operator can securely gain error-free secret data, we design the location index sequences as special keys, which guarantee the algorithm reversibility and enhance the security of the algorithm at the same time. Experimental results show that our algorithm has superior embedding effectiveness compared to some state-of-the-art RDH methods.

关键词： Reversible data hiding Invalid shifting pixels local complexity Location index mechanism Multipeak embedding

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Using local complexity to Accelerate Screen Space Ambient Occlusion

Using Local Complexity to Accelerate Screen Space Ambient Oc...

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13th International Conference on Computer-Aided Design and Computer Graphics (CAD/Graphics)

作者： Cheng, Geng Qiu, Xiangsong Zhang, Yanci Sichuan Univ Chengdu 610064 Peoples R China

ISBN: (纸本)9781479925766

In this paper, we introduce a novel method to accelerate Screen Space Ambient Occlusion(SSAO) by exploiting spatial and temporal coherence. SSAO computation only depends on the surrounding pixels which is modeled as screen space local complexity function in this paper. Two pixels with similar local complexity should have close Ambient Occlusion(AO) value. Based on the above observation, the AO value of pixel p in the current frame might be directly adopted from some pixel p' in previous frame. The experimental results indicate that our algorithm can improve the performance of SSAO by 20-40 percent but achieve the similar visual effect.

关键词： ambient occlusion local complexity frame-to-frame coherence

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Extracting Sea-Sky-Line Based on Improved local complexity

Extracting Sea-Sky-Line Based on Improved Local Complexity

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2010 International Conference on Computer,Mechatronics,Control and Electronic Engineering(2010计算机、机电、控制与电子工程国际会议 CMCE 2010)

作者： Yue-fang DONG An-bang WANG Chao ZHU Yun-feng ZHANG Changchun Institute of Optics Fine Mechanics and PhysicsChinese Academy of Sciences130033Graduate Changchun Institute of Optics Fine Mechanics and PhysicsChinese Academy of Sciences ChangchunChina

Sea-sky-line extraction under complicated sea-sky background is an important aspect of longrange target tracking research. An algorithm based on improved local complexity is proposed according to the feature that the sky and the sea usually show up at different gray levels in sea-sky background images. Median filter is applied first to remove peak noise, and then the point set of sea-sky-line region which has the greatest change in image is obtained by calculating the local complexity of image and selecting a segmentation threshold. Finally, Hough transform is used to extract the sea-sky-line. The experiment results indicate that this method can extract sea-sky-line under simple and complicated sea-sky backgrounds, which is robust and can improve noise immunity of the algorithm.

关键词： sea-sky-line local complexity median filter Hough transform

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Estimating Scene complexity by One and Two local Observations

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AUTOMATION AND REMOTE CONTROL 2019年第8期80卷 1471-1486页

作者： Karkishchenko, A. N. Res & Design Bur Robot & Control Syst Taganrog Russia

The formal problem to estimate the complexity of a scene with numerous obstacles and mobile objects is considered. By assumption there is only limited information on the location of obstacles in a small part of the scene, which is obtained by the sensor systems of one or more objects. Upper and lower bounds for the complexity of the scene are derived for one and two observations of the local domains.

关键词： mobile object scene triangulation local complexity integral complexity complexity estimation

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High-Performance Optimization Framework for Reversible Data Hiding Predictor

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IEEE SIGNAL PROCESSING LETTERS 2025年 32卷 231-235页

作者： Ma, Bin Duan, Hongtao Ma, Ruihe Xian, Yongjin Li, Xiaolong Qilu Univ Technol Shandong Comp Sci Ctr Key Lab Comp Power Network & Informat Secur Minist EducShandong Acad Sci Jinan 250014 Peoples R China Shandong Fundamental Res Ctr Comp Sci Shandong Prov Key Lab Ind Network & Informat Syst Jinan 250353 Peoples R China Univ Hong Kong Business Sch Hong Kong Peoples R China Beijing Jiaotong Univ Sch Comp & Informat Technol Beijing 100044 Peoples R China

Existing deep learning-based reversible data hiding (RDH) predictors are affected by the difference of pixel complexity, which leads to the reduction of prediction accuracy. Therefore, this letter proposes an optimization framework tailored for RDH predictors, which integrates the local complexity of pixels into the predictor's regression optimization process. By analyzing the image's texture features, the framework adaptively determines the optimal prediction coefficients, thereby improving prediction accuracy. Notably, this optimization framework is versatile and can be applied to optimize other deep learning-based RDH predictors. Additionally, recognizing the critical role of interpolation strategies in RDH pixel prediction, we introduce a multi-scale fusion-enhanced interpolation network specifically designed for RDH, which integrates features across different scales to provide accurate reference pixels for subsequent predictions. Finally, experimental results demonstrate that the proposed method outperforms several advanced RDH predictors in terms of both prediction accuracy and embedding performance.

关键词： Interpolation complexity theory Accuracy Optimization Data mining Feature extraction Convolutional neural networks Prediction algorithms Kernel Deep learning local complexity prediction accuracy reversible data hiding

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Understanding Effects of Architecture Design to Invariance and complexity in Deep Neural Networks

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IEEE ACCESS 2021年 9卷 9670-9681页

作者： Kim, Dongha Kim, Yongdai Sungshin Womens Univ Dept Stat Seoul 02844 South Korea Seoul Natl Univ Dept Stat Seoul 08826 South Korea Seoul Natl Univ Dept Data Sci Seoul 08826 South Korea

This article studies the role of architecture design, i.e. choice of the number of nodes at each hidden layer, in deep neural networks (DNNs). We give a theoretical explanation that invariance and complexity of a DNN are determined by the design of its architecture. To be more specific, for DNNs with the rectified linear activation function, we prove that the variations of gradients become the largest when the bottleneck layer, the layer with the fewest nodes, changes its activation pattern and the gap of the variations of gradients of the bottleneck layer to those of the other layers is getting larger as the differences of node counts between the bottleneck layer and the other layers become larger. That is, most large variations of the function made by a DNN are concentrated on the decision boundaries corresponding to the activation patterns of the bottleneck layer. An important implication is that invariance and complexity can be improved simultaneously, which is a key to success of DNNs for many artificial intelligence domains, by designing the highest layer as the bottleneck layer. Various experiments are carried out to validate our theoretical result and its by-products.

关键词： complexity theory Neural networks Deep learning Licenses Indexes Two dimensional displays Standards Deep neural networks local complexity gradient difference

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Estimation of global and local complexities of brain networks: A random walks approach

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NETWORK NEUROSCIENCE 2020年第3期4卷 575-594页

作者： Sotero, Roberto C. Sanchez-Rodriguez, Lazaro M. Moradi, Narges Dousty, Mehdy Univ Calgary Hotchkiss Brain Inst Calgary AB Canada Univ Calgary Dept Radiol Calgary AB Canada Univ Calgary Biomed Engn Grad Program Calgary AB Canada Univ Toronto Inst Biomat & Biomed Engn Toronto ON Canada Univ Hlth Network Toronto Rehab KITE Toronto ON Canada

The complexity of brain activity has been observed at many spatial scales and has been proposed to differentiate between mental states and disorders. Here we introduced a new measure of (global) network complexity, constructed as the sum of the complexities of its nodes (i.e., local complexity). The complexity of each node is obtained by comparing the sample entropy of the time series generated by the movement of a random walker on the network resulting from removing the node and its connections, with the sample entropy of the time series obtained from a regular lattice (ordered state) and a random network (disordered state). We studied the complexity of fMRI-based resting-state networks. We found that positively correlated (pos) networks comprising only the positive functional connections have higher complexity than anticorrelation (neg) networks (comprising the negative connections) and the network consisting of the absolute value of all connections (abs). We also observed a significant correlation between complexity and the strength of functional connectivity in the pos network. Our results suggest that the pos network is related to the information processing in the brain and that functional connectivity studies should analyze pos and neg networks separately instead of the abs network, as is commonly done.

关键词： Network complexity local complexity Random walk Sample entropy Resting-state networks

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On Finding the complexity of an Environment for the Operation of a Mobile Object on a Plane

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AUTOMATION AND REMOTE CONTROL 2019年第5期80卷 897-912页

作者： Karkishchenko, A. N. Pshikhopov, V. Kh Res & Design Bur Robot & Control Syst Taganrog Russia

We consider the problem of formally finding the complexity parameters of the environment for a moving object operating on a plane in the presence of obstacles. We give a mathematical justification for the method of calculating the complexity, introduce the concepts of local and integral complexities of the environment, give analytic formulas for calculating the complexity, and show simulation results.

关键词： moving object scene triangulation local complexity integral complexity

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