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The biggest challenge in image set compression is how to efficiently remove the set redundancy among images as well as the redundancy inside a single image. Different from all the previous schemes, in this paper we are the first to propose a generic image set compression scheme which removes the set redundancy based on local features in addition to luminance values. The SIFT (Scale Invariant Feature...
The complexity of an image tells many aspects of the image content and is an important factor in the selection of source material for testing various image processing methods. We explore objective measures of complexity that are based on compression. We show that spatial information (SI) measures strongly correlate with compression-based complexity measures. Among the commonly used SI measures, the...
The advance in multimedia technologies have resulted in an explosive growth of pictures in personal computers and in cloud. Typically many pictures taken in the same occasion are similar. The cost to store and transmit them can be very significant. Thus it is important to find an efficient method to store these pictures. This paper proposed a compression scheme for similar images. Our approach is...
As compressed sensing can capture signal at sub-Nyquist rate, it is suitable to apply multi-view compressed imaging framework in vision sensor networks. The image views in such networks are correlated with each other, and therefore the performance of independent view reconstruction can be further improved by joint reconstruction. In this paper, we propose a joint reconstruction algorithm, where disparity...
Binocular suppression theory states that the stereo vision quality is not much influenced by asymmetric degradation of the individual views. Based on these findings, mixed resolution (MR) multi-view framework jointly utilizes the lower and full resolution images to reduce the data amount, while maintaining good stereo vision quality. To enhance the resolution of the lower resolution image, a novel...
Compressive sensing (CS) is a new emerging approach in recent years, and is applied in acquisition of signals having a sparse or compressible representation in some basis. The CS literature has mostly focused on the problems involving 1-D signals and 2-D images. However, for hyperspectral image, compressive acquisition of this signal is complicated for its 3-D structures. In this paper, we consider...
This paper proposes a novel flatness measure for video steganalysis targeting LSB based motion vector steganography. The proposed method has introduced two major improvements. Firstly, unlike previous approaches, it takes into account the anchor frame and current frame distances and directions, which significantly affect the correlation strength of adjacent motion vectors. Secondly, it defines a cover...
In this paper, we propose block level video steganalysis method. Current steganalysis methods detect steganograms at frame level only. In this paper, we present a new steganalysis method using correlation of pattern noise between consecutive frames as feature. First we extract the pattern noise from each frame and obtain difference between consecutive frames pattern noise. Later we divide the difference...
Hierarchical transforms are widely used in image and video coding to produce multilevel decomposition of signals. After applying these transforms, same level signals are typically uncorrelated. However, there is often still significant cross level information. Traditionally, this cross-level information is exploited at the entropy coding step, but not at the transform step. The main contribution of...
Copy-move is a common used forgeries in digital image tampering. In this paper, a non block-matching based fast method to detect image copy-move forgery is proposed exploiting phase correlation. Results of experiments indicate that the proposed method is valid in detecting the image region duplication and quite robust to additive noise and blurring.
The efficiency, robustness and distinctiveness of a feature descriptor are critical to the user experience and scalability of a mobile Augmented Reality (AR) system. However, existing descriptors are either too compute-expensive to achieve real-time performance on a mobile device such as a smartphone or tablet, or not sufficiently robust and distinctive to identify correct matches from a large database...
This paper presents a block-based low-complexity screen compression scheme. In this scheme, the input screen is split into non-overlapping blocks which are classified as pictorial blocks and textual blocks. We design a low-complexity yet efficient algorithm to compress the textual blocks. We use base colors plus escape pixels to represent and quantize the text pixels, and such quantization mechanism...
The Chemical Imaging Initiative at the Pacific Northwest National Laboratory (PNNL) is creating a ‘Rapid Experimental Analysis’ (REXAN) Framework, based on the concept of reusable component libraries. REXAN allows developers to quickly compose and customize high throughput analysis pipelines for a range of experiments, as well as supporting the creation of multi-modal analysis pipelines. In addition,...
As digital chaos is affected by the limited precision of computer, chaotic sequence properties present short periodicity. For the phenomenon of digital chaos, the paper analyzes similar-short periodicity of digital chaos and puts forward a novel generator of digital chaos key sequence. Meanwhile, we detail the DCT (Discrete Cosine Transformation) compression and Wavelet compression method, and then...
A kind of RGB-based compression coding algorithm is proposed to gain better recovered image and efficiently remove the correlation of components in colored image. The key of algorithm is its complete DCT transformation for components of RGB. With table lookup in DCT transformation, the advantage of table lookup can have better embodiment. Result of empirical analysis on spatial coding performance...
This papers presents a novel steganalysis scheme for YouTube compressed video. The proposed scheme exploits spatial-temporal correlation between adjacent frames. To this end, 3D Discrete Cosine Transform (DCT) is used to capture this correlation. Statistical features sensitive to data hiding such as absolute central moments, skewness, kurtosis, and Markov features are used as classifying statistics...
This paper proposes a new method of inter prediction based on low-rank matrix completion. By collection and rearrangement, image regions with high correlations can be used to generate a low-rank or approximately low-rank matrix. We view prediction values as the missing part in an incomplete low-rank matrix, and obtain the prediction by recovering the generated low-rank matrix. Taking advantage of...
The source camera identification problem is concerned with the identification of the camera that has been used to generate a digital picture. A widely adopted identification technique, proposed by Lukas in [1], relies on the usage of the pattern noise left by the camera sensor as a fingerprint. This technique may perform badly when applied to images that have undergone lossy compression techniques,...
Although wireless sensor networks (WSNs) are powerful in monitoring physical events, the data collected from a WSN are almost always incomplete if the surveyed physical event spreads over a wide area. The reason for this incompleteness is twofold: i) insufficient network coverage and ii) data aggregation for energy saving. Whereas the existing recovery schemes only tackle the second aspect, we develop...
This paper presents a new approach for watermarking of digital video MPEG-4 providing robustness against geometric distortion and JPEG compression at the same time with blind detection remains especially challenging. We propose in this work a robust video object based watermarking scheme with scrambled watermark, based on three dimensional Walsh Hadamard Transform (3D-WHT) and Discrete Wavelet Transform...
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