Genuine Reversible Data Hiding Technique for H.264 Bitstream Using Multi-Dimensional Histogram Shifting Technology on QDCT Coefficients

Video has become the most important medium for communication among people. Video has become the most important medium for communication among people. Therefore, reversible data hiding technologies for video have been developed so that information can be hidden in the video without damaging the original video in order to be used in the copyright protection and distribution field of video. This paper proposes a practical and genuine reversible data hiding method by using a multi-dimensional histogram shifting scheme on QDCT coefficients in the H.264/AVC bitstream. The proposed method defines the vacant histogram bins as a set of n-dimensional vectors and finds the optimal vector space, which gives the best performance, in a 4 × 4 QDCT block. In addition, the secret message is mapped to the optimal vector space, which is equivalent to embedding the information into the QDCT block. The simulation results show that the data hiding efficiency is the highest among the compared five existing methods. In addition, the image distortion and maximum payload capacity are measured quite high.

Object Detection in Cluttered Background using Color Clusters

Object detection in presence of complex background and illumination variation is important image analysis problem with many applications. Most of the object detection algorithms use local image descriptors which are computed from interest points based on luminance information and neglect precious color information of an object. If appearances of the object to be detected contain multiple colors in non-homogeneous distributions then it makes it difficult to detect these objects using shape features. In this context, we propose a robust algorithm designed to detect a class of objects using a descriptor which is computed from color information of an object. Clusters are formed in Hue and Saturation (HS) color space of an object using k-means clustering and cluster analysis based on number of pixels belong to each cluster, object detection is performed. Use of clustering algorithm in color space of an object to form descriptor reduces the large dimensionality of the histogram bins in the computation. The performance of the algorithm is demonstrated by experimentation carried out on standard dataset GroZi-120. Experimental results shows that the proposed algorithm is insensitive to scaling, object rotation, illumination variations and capable of handling cluttered background effectively. Finally results shows that proposed algorithm outperforms closely related algorithm by a decisive margin.

DyS: A Framework for Mixture Models in Quantification

Quantification is an expanding research topic in Machine Learning literature. While in classification we are interested in obtaining the class of individual observations, in quantification we want to estimate the total number of instances that belong to each class. This subtle difference allows the development of several algorithms that incur smaller and more consistent errors than counting the classes issued by a classifier. Among such new quantification methods, one particular family stands out due to its accuracy, simplicity, and ability to operate with imbalanced training samples: Mixture Models (MM). Despite these desirable traits, MM, as a class of algorithms, lacks a more in-depth understanding concerning the influence of internal parameters on its performance. In this paper, we generalize MM with a base framework called DyS: Distribution y-Similarity. With this framework, we perform a thorough evaluation of the most critical design decisions of MM models. For instance, we assess 15 dissimilarity functions to compare histograms with varying numbers of bins from 2 to 110 and, for the first time, make a connection between quantification accuracy and test sample size, with experiments covering 24 public benchmark datasets. We conclude that, when tuned, Topsøe is the histogram distance function that consistently leads to smaller quantification errors and, therefore, is recommended to general use, notwithstanding Hellinger Distance’s popularity. To rid MM models of the dependency on a choice for the number of histogram bins, we introduce two dissimilarity functions that can operate directly on observations. We show that SORD, one of such measures, presents performance that is slightly inferior to the tuned Topsøe, while not requiring the sensible parameterization of the number of bins.

Commutative Watermarking-Encryption of Multimedia Data Based on Histograms

Histogram-based watermarking schemes are invariant to pixel permutations and can thus be combined with permutation-based ciphers to form a commutative watermarking-encryption scheme. In this chapter, the authors demonstrate the feasibility of this approach for audio data and still image data. Typical histogram-based watermarking schemes based on comparison of histogram bins are prone to desynchronization attacks, where the whole histogram is shifted by a certain amount. These kind of attacks can be avoided by synchronizing the embedding and detection processes, using the mean of the histogram as a calibration point. The resulting watermarking scheme is resistant to three common types of shifts of the histogram, while the advantages of previous histogram-based schemes, especially commutativity of watermarking and permutation-based encryption, are preserved. The authors also report on the results of testing robustness of the still image watermark against JPEG and JPEG2000 compression and on the possibility of using histogram-based watermarks for authenticating the content of an image.

A Reversible Watermarking Scheme for Vector Maps Based on Multilevel Histogram Modification

To protect the security of vector maps, we propose a novel reversible watermarking scheme for vector maps based on a multilevel histogram modification. First, a difference histogram is constructed using the correlations of adjacent coordinates, and the histogram is divided into continuous regions and discontinuous regions by combining the characteristics of vector map data. Second, the histogram bins that require modification are determined in the continuous regions through the optimal peak value, and the peak values are chosen from the flanking discontinuous regions in both directions; the watermarks are embedded by adopting the multilevel histogram modification strategy. The watermark extraction process is the reverse of the embedding process, and after completing the watermark extraction, the carrier data can be recovered losslessly. The experimental results show that the proposed algorithm has good invisibility and is completely reversible. Compared with similar algorithms reported previously, it achieves higher watermark embedding capacity under the same embedding distortion with lower complexity, thereby having a higher application value.

Contrasting the co-variability of daytime cloud and precipitation over tropical land and ocean

The co-variability of cloud and precipitation in the extended tropics (35∘ N–35∘ S) is investigated using contemporaneous data sets for a 13-year period. The goal is to quantify potential relationships between cloud type fractions and precipitation events of particular strength. Particular attention is paid to whether the relationships exhibit different characteristics over tropical land and ocean. A primary analysis metric is the correlation coefficient between fractions of individual cloud types and frequencies within precipitation histogram bins that have been matched in time and space. The cloud type fractions are derived from Moderate Resolution Imaging Spectroradiometer (MODIS) joint histograms of cloud top pressure and cloud optical thickness in 1∘ grid cells, and the precipitation frequencies come from the Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) data set aggregated to the same grid. It is found that the strongest coupling (positive correlation) between clouds and precipitation occurs over ocean for cumulonimbus clouds and the heaviest rainfall. While the same cloud type and rainfall bin are also best correlated over land compared to other combinations, the correlation magnitude is weaker than over ocean. The difference is attributed to the greater size of convective systems over ocean. It is also found that both over ocean and land the anti-correlation of strong precipitation with “weak” (i.e., thin and/or low) cloud types is of greater absolute strength than positive correlations between weak cloud types and weak precipitation. Cloud type co-occurrence relationships explain some of the cloud–precipitation anti-correlations. Weak correlations between weaker rainfall and clouds indicate poor predictability for precipitation when cloud types are known, and this is even more true over land than over ocean.

Geometrically Invariant Image Watermarking Using Histogram Adjustment

In this article, a blind image watermarking scheme, which is a robust against common image processing and geometric attacks is proposed by adopting the concept of histogram-based embedding. The average filter is employed to low-pass pre-filter the host image. The watermark bits are embedded into the histogram of the low-frequency component and the template bits are embedded in the high-frequency residual. The embedding is performed by adjusting the value of two consecutive histogram bins. Furthermore, a post-quantization is employed after the embedding round to improve robustness. All pixel modifications incurred are based on the human visual system (HVS) characteristics. As a result, a good tradeoff between robustness and imperceptibility is achieved. Experimental results reported the satisfactory performance of the proposed scheme with respect to both common image processing and geometric attacks.

Contrasting the Co-variability of Daytime Cloud and Precipitation over Tropical Land and Ocean

The co-variability of cloud and precipitation in the extended tropics (35° N–35° S) is investigated using contemporaneous datasets for a 13-year period. The goal is to quantify the relationship between cloud types and precipitation events of particular strength. Particular attention is paid to whether the relationship exhibits different characteristics over tropical land and ocean. A major analysis metric is correlation coefficients between fractions of individual cloud types and frequencies within precipitation histogram bins that have been matched in time and space. The cloud type fractions are derived from Moderate Resolution Imaging Spectroradiometer (MODIS) joint histograms of cloud top pressure and cloud optical thickness in one-degree grid cells, and the precipitation frequencies come from the Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) dataset aggregated to the same grid. It is found that the strongest coupling (positive correlation) between clouds and precipitation occurs for cumulonimbus clouds and heaviest rainfall over ocean. While the same cloud type and rainfall bin are also best correlated over land compared to other combinations, the correlation magnitude over land is weaker than over ocean. The difference is attributed to the greater size of convective systems over ocean. It is also found both over ocean and land that the anti-correlation of strong precipitation with weak (i.e., thin and/or low) cloud types is of greater absolute strength than positive correlations between weak cloud types and weak precipitation. Cloud type co-occurrence relationships explain some of the cloud-precipitation anti-correlations. Couplings between weaker rainfall and clouds are also distinct in ocean vs. land, with precipitation predictability when cloud type is known being quite poor in general, particularly over land.