Context Of Pixel Research Articles

Dual-complexities based on straightforward neighborhood pixel prediction in pixel-value-ordering framework for reversible data hiding

PVO-based schemes are the widely-used reversible data hiding (RDH) techniques. Benefiting from the good prediction performance, the stego-image can have a high quality. However, the complexity metric of PVO is still not good enough. The two main limitations are: the block-based context pixels are not highly correlated with the predicted pixel, and the fluctuation-based complexity calculation methods cannot comprehensively represent the real prediction result. Unlike the existing complexity metrics, we consider this problem from a novel viewpoint of neighborhood pixel prediction (NPP), i.e., using the prediction pixel to predict the unmodified neighborhood pixels of a predicted pixel. The neighborhood pixels are more reliable than the context pixels and the generated neighborhood prediction-errors (NPEs) are utilized to represent the real prediction-error (RPE). Two new features are extracted from NPEs as Dual-complexities to determine the embedding order. Experimental results indicate the quality of the stego-image can be improved significantly by using our proposed Dual-complexities in the related PVO-based schemes, and it can be directly extended to other schemes in PVO framework as well.

Weighted Average-Based Complexity Calculation in Block Selection Oriented Reversible Data Hiding

Complexity of blocks is one of the key factors to influence the performance of reversible data hiding. By embedding data into blocks with smaller complexity, the invalid shifting in pixel value ordering (PVO) based reversible data hiding is largely reduced, thus increasing the imperceptibility. However, the existing techniques are not always accurate in complexity calculation, since context pixels are usually treated equally. As a result, a great number of invalid pixels are assigned too large a weight in complexity calculation, which inevitably affects the precision. In this paper, we propose a weighted average based novel strategy in complexity calculation. In this scheme, we calculate the weight of a certain context pixel with the help of its adjacent neighbors, which ensures the one in a smooth area is assigned a larger weight, and otherwise the opposite. Experimental results show that invalid shifting is largely reduced, thus our scheme is better in imperceptibility compared with the state of the art and has potential application in a lot of fields such as medical imaging.

Non-Local Feature Search Network for Building and Road Segmentation of Remote Sensing Image

Building and road extraction from remote sensing images is of great significance to urban planning. At present, most of building and road extraction models adopt deep learning semantic segmentation method. However, the existing semantic segmentation methods did not pay enough attention to the feature information between hidden layers, which led to the neglect of the category of context pixels in pixel classification, resulting in these two problems of large-scale misjudgment of buildings and disconnection of road extraction. In order to solve these problem, this paper proposes a Non-Local Feature Search Network (NFSNet) that can improve the segmentation accuracy of remote sensing images of buildings and roads, and to help achieve accurate urban planning. By strengthening the exploration of hidden layer feature information, it can effectively reduce the large area misclassification of buildings and road disconnection in the process of segmentation. Firstly, a Self-Attention Feature Transfer (SAFT) module is proposed, which searches the importance of hidden layer on channel dimension, it can obtain the correlation between channels. Secondly, the Global Feature Refinement (GFR) module is introduced to integrate the features extracted from the backbone network and SAFT module, it enhances the semantic information of the feature map and obtains more detailed segmentation output. The comparative experiments demonstrate that the proposed method outperforms state-of-the-art methods, and the model complexity is the lowest.

A Shallow Prediction Model with Enhanced Features for Video Time Series

Fast video time series prediction is an important part in video anomaly detection in deep learning framework. Time series deep learning models have good performance in time-series data prediction, such as traffic flow, rainfall and network traffic. The models, such as ConvLSTM, ConvGRU with complex structure and large amount of computation, are difficult to fit the video time series prediction. In this paper, we analyze data characteristics of video sequences and propose a shallow video series prediction model. We not only consider the spatial correlation in continuous video frames, but also investigate the interval video frames, which also maintain spatial correlation. We explore the gray scale pixel values of the interval frames which appear specific trend properties when we connect the context pixels together. Based on the enhanced features, we design a shallow 2D convolutional video time series prediction model with good prediction performance and much less total parameters.

Adaptive Complexity for Pixel-Value-Ordering Based Reversible Data Hiding

Pixel-value-ordering (PVO) is a widely used reversible data hiding (RDH) framework which aims to achieve the high quality of stego-image under low capacity. In this letter, we propose a general location-based adaptive complexity for PVO. Different from the block-based complexity in the previous PVO-based methods, our proposed method adaptively selects context pixels from the perspective of the relative locations of predicted pixel and prediction pixel. Consequently, different number of high correlation context pixels can be adaptively selected and the context pixels can break the limitation of the current block. Moreover, instead of sharing the same block complexity by two predicted pixels in the current block, each predicted pixel can be utilized independently according to its own corresponding complexity. Our proposed adaptive complexity can combine with any PVO-based methods and the experimental results show that our proposed method achieves a significant improvement in prediction accuracy and embedding performance.

An Invalid Cloud Region Masking Method for Remote Sensing Image Compression

An invalid cloud region masking method based on local edge context is proposed for the compression of remote sensing images. Through analyzing the characteristics of various compression algorithms and taking the local edge information of the cloud region into account, the decompression quality is improved. First, according to the cloud mask information, labeling the connected cloud region, second, performing region growth on the labeled mask image, then differing the two mask images to obtain the local edge context, and finally different invalid cloud regions are filled with the average of the respective local edge context pixels. Using the image testing set generated from QuickBird and OrbView images, our method’s impact on six common remote sensing image compression algorithms is analyzed experimentally. The experimental results show that our masking method can improve the decompressed image quality when the compression ratio is certain. When the image quality is fixed, it can further reduce the compressed bitrate. For onboard application, our masking method can increase the onboard imaging time of the satellites, and eventually improve the onboard specifications of remote sensing satellites.

Minimum barrier distance based tracking via spatio-temporal context learning

We propose an efficient and robust tracking method based on minimum barrier distance (MBD) and spatio-temporal context (STC) learning. It is robust to noise and blur, and can be evaluated approximately through a Dijkstra-like algorithm, leading to fast computation. We adopt the MBD transform to measure the weights of context pixels, and formulate the spatio-temporal relationship between the object and its surrounding region based on a Bayesian framework to estimate the most likely location of the target. A bounded scale update model is proposed to estimate the size of the object. The whole proposed method runs at nearly 160 frames per second (FPS) on an i5 machine. Extensive experimental results show it has comparable or better comprehensive performance than the original STC and some other leading methods.

Improved reversible data hiding using pixel-based pixel value grouping

Pixel value grouping (PVG) has been proved effective in exploiting block redundancy. However, the uniform pixel block still exposes the deficiency in generating prediction errors in a block-by-block manner. In this paper, a novel pixel-based pixel value grouping (PPVG) is proposed with which the embedding primitive switches from pixel block to pixel and PVG is fulfilled on context pixels to calculate predicted value. In this way, not only the advantage of PVG can be preserved but also all pixels can be fully utilized and classified. Then more and smaller expandable prediction errors can be obtained by utilizing pixels in smooth regions with priority. As a result, the embedding procedure can embed more data with less distortion. Experimental results verify that the proposed scheme outperforms other state-of-the-art schemes.

An Improved Histogram Modification Based Reversible Information Hiding Scheme

ABSTRACTIn this paper, the authors propose an accurate prediction scheme to improve the histogram modification based reversible information hiding (HMRIH) algorithm. The prediction is based on both local gradients and context pixels. The proposed scheme detects not only the existence of an edge (vertical or horizontal), but also its strength (sharp, normal, or weak). Since such an approach can generate more accurate prediction values, the prediction errors tend to form a much sharper histogram distribution. The experimental results reveal that, without degrading the image quality, the proposed scheme can improve the embedding capacity by 7.9% in average.

Obtuse Angle Prediction and Factor Evaluation for Image Reversible Data Hiding

A pixel-based pixel-value-ordering (PPVO) has been used for reversible data hiding to generate large embedding capacity and high-fidelity marked images. The original PPVO invented an effective prediction strategy in pixel-by-pixel manner. This paper extends PPVO and proposes an obtuse angle prediction (OAP) scheme, in which each pixel is predicted by context pixels with better distribution. Moreover, for evaluating prediction power, a mathematical model is constructed and three factors, including the context vector dimension, the maximum prediction angle, and the current pixel location, are analyzed in detail. Experimental results declare that the proposed OAP approach can achieve higher PSNR values than PPVO and some other state-of-the-art methods, especially in the moderate and large payload sizes.

Hybrid Predictor and Field-Biased Context Pixel Selection Based on PPVO

Most pixel-value-ordering (PVO) predictors generated prediction-errors including −1 and 1 in a block-by-block manner. Pixel-based PVO (PPVO) method provided a novel pixel scan strategy in a pixel-by-pixel way. Prediction-error bin 0 is expanded for embedding with the help of equalizing context pixels for prediction. In this paper, a PPVO-based hybrid predictor (HPPVO) is proposed as an extension. HPPVO predicts pixel in both positive and negative orientations. Assisted by expansion bins selection technique, this hybrid predictor presents an optimized prediction-error expansion strategy including bin 0. Furthermore, a novel field-biased context pixel selection is already developed, with which detailed correlations of around pixels are better exploited more than equalizing scheme merely. Experiment results show that the proposed HPPVO improves embedding capacity and enhances marked image fidelity. It also outperforms some other state-of-the-art methods of reversible data hiding, especially for moderate and large payloads.

A Low Distortion Reversible Data Hiding Technique Using Improved PPVO Predictor

Reversible data hiding is a technique that embeds additional information into some distortion-unacceptable cover media, such as military images, in a reversible manner so that the original cover image can be restored after extraction of the hidden information. This work extends a recently proposed reversible data hiding (RDH) scheme of Qu et al. which is based on pixel-based pixel- value-ordering(PPVO) and prediction-error expansion. In Qu et al.’s method, each pixel is predicted using its sorted context pixels. In this work, the pixel neighborhood of each pixel is expanded to optimize the embedding performance in terms of capacity- distortion behavior. This can better exploit image redundancy; achieve superior embedding performance and low distortion. Thus, the proposed method is able to embed adequate data into a cover image with limited distortion. The superiority of this predictor is verified through extensive experimental results. The proposed method outperforms prior works in terms of PSNR. The PSNR of a modified image versus its original one is guaranteed to be above 57.0dB.

Pixel-based pixel value ordering predictor for high-fidelity reversible data hiding

A pixel-value-ordering (PVO) predictor has been used in reversible data hiding methods to produce high-fidelity marked images when the payload size is moderate. The original PVO generates predictions in a block-by-block manner where only two pixels can be predicted in each block. In this paper, a novel pixel-based PVO (PPVO) is proposed with which each pixel is predicted using its sorted context pixels. The benefit of the PPVO is that the pixels in smooth image regions can be better used to embed data, while most of those pixels are neglected in PVO due to the block constraint. Experimental results demonstrate that the embedding capacity of PPVO is larger than that of PVO, especially for relatively smooth images. The obtained marked image quality of PPVO is also better than that of PVO and other state-of-the-art reversible data hiding methods.

Design of a Spectral–Spatial Pattern Recognition Framework for Risk Assessments Using Landsat Data—A Case Study in Chile

For many ecological applications of remote sensing, traditional multispectral data with moderate spatial and spectral resolution have to be used. Typical examples are land-use change or deforestation assessments. The study sites are frequently too large and the timespan covered too long assumes the availability of modern datasets such as very high resolution or hyperspectral data. However, in traditional datasets such as Landsat data, separability of the relevant classes is limited. A promising approach is to describe the landscape context pixels that are integrated. For this purpose, multiscale context features are computed. Then, spectral-spatial classification is employed. However, such approaches require sophisticated processing techniques. This study exemplifies these issues by designing an entire framework for exploiting context features. The framework uses kernel-based classifiers which are unified by a multiple classifier system and further improved by conditional random fields. Accuracy on three scenarios is raised between 19.0%pts and 26.6%pts. Although the framework is designed, focusing an application in Chile, it is generally enough to be applied to similar scenarios.

Improved Embedding for Prediction-Based Reversible Watermarking

This paper aims at reducing the embedding distortion of prediction error expansion reversible watermarking. Instead of embedding the entire expanded difference into the current pixel, the difference is split between the current pixel and its prediction context. The modification of the context generates an increase of the following prediction errors. Global optimization is obtained by tuning the amount of data embedded into context pixels. Prediction error expansion reversible watermarking schemes based on median edge detector (MED), gradient-adjusted predictor (GAP), and a simplified GAP version, SGAP, are investigated. Improvements are obtained for all the predictors. Notably good results are obtained for SGAP-based schemes. The improved SGAP appears to outperform GAP-based reversible watermarking.