38238585
Exploiting multi-scale support vector regression for image compression
Unlike traditional neural networks that require predefined topology of the network, support vector regression (SVR) approach can model the data within the given level of accuracy with only a small subset of the training data, which are called support vectors (SVs). This property of sparsity has been exploited as the basis for image compression. In this paper, for still image compression, we propose a multi-scale support vector regression (MS-SVR) approach, which can model the images with steep variations and smooth variations very well resulting in good performance. We test our proposed MS-SVR based algorithm on some standard images. The experimental results verify that the proposed MS-SVR achieves better performance than standard SVR. And in a wide range of compression ratio, MS-SVR is very close to JPEG in terms of peak signal-to-noise ratio (PSNR) but exhibits better subjective quality. Furthermore, MS-SVR even outperforms JPEG on both PSNR and subjective quality when the compression ratio is higher enough, for example 25:1 for Lena image. Even when compared with JPEG-2000, the results show greatly similar trend as those in JPEG experiments, except that the compression ratio is a bit higher where our proposed MS-SVR will outperform JPEG-2000.
709845
Supporting Live Video on Ad Hoc Wireless Networks: Unbalanced Multiple Description Coding, Single Path Transport and Recursive Error Concealment
Robust transmission of live video over ad hoc wireless networks presents new challenges: high bandwidth requirements are coupled with delay constraints; nodes are constrained in processing power and storage capacity; ad hoc wireless networks suffer from bursty packet losses that drastically degrade the viewing experience. Accordingly, we propose a simple, but practical unbalanced multiple description (UMD) codec that uses single path transport only. At the UMD decoder, the lost Low- Resolution (LR) frames can be receded from the corresponding ones of the correctly received High- Resolution (HR) stream. This makes LR more robust to packet losses and ensures continuous video playback. The simulation results show that the proposed UMD codec has higher decoded quality, smaller quality fluctuation and lower probability of pause than other error resilience techniques, especially when channel burstiness becomes large. We also propose a novel sequence-based error concealment (EC) algorithm for our UMD decoder. It recursively uses multi-frame recovery principle to frame-by-frame reduce error drift in the HR description with respect to the LR description. In fact, the algorithm can be applied to most UMD schemes. It has the advantage of working on integer-, half- or quarter- pixel precision and looking backward without buffering future frames. Experimental results show that error decreases the fastest if the UMD decoder uses our EC algorithm. It can provide satisfactory performance on both objective and subjective evaluation.
28434248
Optimized Rate Allocation for Unbalanced Multiple Description Video Coding Over Unreliable Packet Network
35782406
License management scheme with anonymous trust for digital rights management
One of the major issues raised by digital rights management systems concerns the protection of the user's privacy and anonymous consumption of content. However, most existing license management schemes for DRM systems do not support the protection of user privacy. Moreover, some other schemes such as PrecePt can only bind the license with a specified device though they concern privacy protection. In this paper, we propose a license management scheme named LMSAT (License Management Scheme with Anonymous Trust) which provides a more powerful and flexible license acquisition and usage tracking scheme to allow the user access the contents anytime, anywhere, and on any compliant devices anonymously.
8253940
A pinhole camera modeling of motion vector field for tennis video analysis
The motion vector field (MVF) represents motion characteristics in video sequences, and has been widely used and been proved to be effective in sports video analysis. However, in tennis video analysis, MVF-based methods are seldom utilized for the reasons that: (i) the players' true motion is not accurately represented by the extracted motion vector, due to the deformation caused by the diagonal shooting of the camera; and (ii) the motion vector's magnitude is not prominent enough and is prone to be disturbed by noises. In this paper, a pinhole camera modeling of the motion vector field is proposed to revise the deformed motion vector. In this modeling, a foreground object mask is adopted and global motion compensation is incorporated as the pre-processing steps. Evaluation of the proposed modeling using four hours of tennis videos shows very encouraging results.
3441918
Effect of Different Coding Patterns on Compressed Frequency Domain Based Universal JPEG Steganalysis
Current steganalytic schemes for JPEG steganography are in favor of extracting features in DCT (Discrete Cosine Transform) domain and/or spatial domain. A recently proposed compressed frequency domain based universal steganalytic algorithm [21] showed concern over the statistics of Huffman compressed stream. The authors claimed that it was very effective in detecting stego images embedded by JPEG steganographic tools, including JPHide, F5 and OutGuess. Even though only several bytes were embedded, the scheme was still able to work, thus demonstrating astonishing steganalysis capability. By carefully controlled studies on the factors which may have impact on the new steganalytic method, we find out the truly cause of the powerfulness of this "payload-independent" steganalyzer. Experimental results reveal that different coding patterns used in cover and stego images, rather than the Avalanche Criterion [24] explained by its authors, have led to great detection efficiency. When the same coding pattern is employed in both cover and stego images, the performance of the newly devised steganalyzer has greatly dropped. Implication from this paper is that we should ensure the difference between the cover and stego images is only caused by data embedding itself in steganography and steganalysis.
2288383
An Improved FCM Algorithm Incorporating Spatial Information for Image Segmentation
Fuzzy c-means (FCM) clustering algorithm is a popular model widely used in segmentation of magnetic imaging (MRI) data. The conventional FCM does not take into account the spatial information of image and get the unexpected results of segmentation when dealing with some MRI contaminated by noise. Considering the intensities of ideal MRI are piecewise constant, we present an improved model to fuzzy c-means algorithm using membership smoothing constraint. The proposed algorithm can reasonably use the spatial information of image and improve the accuracy of segmentation. Simulation MR brain image with different noise levels and real MR brain image are presented in the experiments. The results of experiments show better robustness of our algorithms to noise than other segmentation algorithms.
196401
An ID-based Anonymous Proxy Signature From Bilinear Pairings
Abstract: A new ID-based anonymous proxy signature scheme from bilinear pairings was proposed in this paper. Anonymous proxy signature is suitable for the situation that the proxy signer’s identity needs to be kept secret. The verifier needs to reveal the real identity of the proxy signer with the help of the original signer. The signature is based on Gap Diffie-Hellman group problems and meets the security requirements such as verifiability, unforgeability, undeniability, anonymity and traceability.
19385120
DJ DreamFactory
DJ DreamFactory is a web-based integrated platform for interactive broadcasting over the Internet. In DJ DreamFactory, users are able to set up Internet-based broadcasting stations with minimal effort. Audience not only can receive broadcasting programs from multiple stations through this unified platform, but can also "talk" and "write" to the broadcasters as well as to other audience via real-time text and/or audio interactions.
39681871
Automated Cellular Modeling and Prediction on a Large Scale
We describe CHAMP (CHurn Analysis, Modeling, andPrediction), an automated system for modeling cellularsubscriber churn that is predicting which customerswill discontinue cellular phone service. We describevarious issues related to developing and deployingthis system including automating data access from aremote data warehouse, preprocessing, featureselection, model validation, and optimization toreflect business tradeoffs. Using data from GTE'sdata warehouse for cellular phone customers, CHAMP iscapable of developing churn models customized byregion for over one hundred GTE cellular phone marketstotaling over 5 million customers. Every month churnfactors are identified for each geographic region andmodels are updated to generate churn scores predictingwho is likely to churn in the short term. Learningmethods such as decision trees and genetic algorithmsare used for feature selection and a cascade neuralnetwork is used for predicting churn scores. Inaddition to producing churn scores, CHAMP alsoproduces qualitative results in the form of rules andcomparison of market trends that are disseminatedthrough a web based interface.