5295208
Bit rate reduction of H.264/AVC video coding for mobile applications
The increasing use of mobile applications requires higher compression efficiency for video coding. However, in mobile applications, the capability of the processor is limited and the channel is unreliable. Since the emerging H.264/AVC video coding standard provides superior compression performance compared to all the existing video coding standards, many research efforts concentrate on the compression efficiency of intra-frame coding in H.264/AVC. In this paper, we propose a novel algorithm to reduce the bit rate compressed by H.264/AVC intra-frame coding. We focus on the method to code the intra-frame prediction modes. Our proposed method significantly reduces the bits used to code the modes and do not change the syntax of the bit stream. Experimental results demonstrate it is effective and efficient
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.
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.
28617398
An Unbalanced Multiple Description Coding Scheme for Video Transmission Over Wireless Ad Hoc Networks
1210599
Joint error concealment and error recovery for consecutive frame losses under the unbalanced multiple description coding architecture
Real-time video transmission over error-prone wireless networks often experiences consecutive frame losses due to either temporary link outages or traffic congestion. Although error concealment (EC) techniques have been extensively studied, they usually cannot handle the problem. Thus, we envision using EC under the unbalanced multiple description coding (UMDC) architecture. UMDC has almost no coding delay and can produce two descriptions at any bit-rates adaptive to different path bandwidths. In this paper, we propose an iterative EC algorithm able to adaptively exploit both the high-resolution (HR) and low-resolution (LR) information via multi-hypothesis weights. It is applied to both lost HR frames and following undecoded ones. Considering error propagation, we design an interframe error recovery (ER) algorithm for the undecoded HR frames. It iteratively uses multi-frame recovery principle to frame-by-frame reduce error drift in the HR stream with respect to the intermediate information from EC. The joint design of EC and ER can be applied to most UMD approaches. Extensive experiments have been carried out under different conditions. The proposed EC technique exhibits high PSNR gains versus the usual ones under the UMDC architecture and the classical ones without the support of UMDC. The proposed ER technique is efficient in reducing error drift. In conclusion, joint EC and ER can provide satisfactory performance on both PSNR and visual quality.
16772027
CCL-SVC: Optimizing user experience of broadcasting video on computation capability limited handheld devices
In this paper, we propose a novel scheme using computing complexity layered scalable video coding (CCLSVC) to optimize the user experience of broadcasting video in the computing capability limited handheld terminals. To address the heterogeneity of computing capability among different handheld devices, we employ hierarchal B reference structure of SVC to divide the frames into multiple computing complexity layers (CC Layers) in server side. The handheld clients simply choose to decode the frames in their corresponding layers in terms of their computation capability to maximize the video PSNR. We have proved that the optimal CC Layers division problem is a precedence constrained scheduling problem, which is an NP-complete problem. And we further propose our fast greedy method to approximately get optimized broadcasting video playback PSNR. The simulation shows that our method is superior to temporal SVC and random frame discarding method.
3451766
Attack LSB Matching Steganography by Counting Alteration Rate of the Number of Neighbourhood Gray Levels
In this paper, we propose a new method for attacking the LSB (least significant bit) matching based steganography. Different from the LSB substitution, the least two or more significant bit-planes of the cover image would be changed during the embedding in LSB matching steganography and thus the pairs of values do not exist in stego image. In our proposed method, we get an image by combining the least two significant bit-planes and divide it into 3x3 overlapped subimages. The subimages are grouped into four types, i.e. T 1,T 2, T 3 and T 4 according to the count of gray levels. Via embedding a random sequence by LSB matching and then computing the alteration rate of the number of elements in T 1, we find that normally the alteration rate is higher in cover image than in the corresponding stego image. This new finding is used as the discrimination rule in our method. Experimental results demonstrate that the proposed algorithm is efficient to detect the LSB matching stegonagraphy on uncompressed gray scale images.
3439449
Classification of functional data: A segmentation approach
We suggest a classification and feature extraction method on functional data where the predictor variables are curves. The method, called functional segment discriminant analysis (FSDA), combines the classical linear discriminant analysis and support vector machine. FSDA is particularly useful for irregular functional data, characterized by spatial heterogeneity and local patterns like spikes. FSDA not only reduces the computation and storage burden by using a fraction of the spectrum, but also identifies important predictors and extracts features. FSDA is highly flexible, easy to incorporate information from other data sources and/or prior knowledge from the investigators. We apply FSDA to two public domain data sets and discuss the understanding developed from the study.
43513684
A representation theorem for recovering contraction relations satisfying wci
A notion of an image structure associated with the canonical epistemic state is introduced. Based on it, we get a representation result for recovering contraction inference relations satisfying the condition weak conjunctive inclusion (wci) in terms of F-standard epistemic AGM states. In effect, this result establishes a representation theorem for belief contraction functions satisfying AGM postulates (k-1) - (k-7), and Rott's (wci) and (k-8c), and hence generalizes Rott's corresponding result in the finite framework.
25955132
A new synchronization algorithm for VHDL-AMS simulation
VHDL-AMS is the Analog and Mixed-Signal Extensions to VHDL. The paper gives a brief overview of the added features to VHDL. A mixed-signal simulator has been developed based on VHDL-AMS. A new synchronization algorithm is adopted in the simulator. Using the new algorithm the analog kernel does not need to synchronize the digital kernel at each digital event time point. The efficiency of the new synchronization algorithm is tested by examples. Simulation results show the newly developed algorithm can speed up the simulation.
17395966
Scene segmentation based on video structure and spectral methods
Scene is an important semantic unit for video analysis, retrieval and browsing. However, due to the lack of a generic algorithm, many studies focus on specific methods for certain video genes, e.g., news, sports, etc. In this paper, we propose a general framework for scene segmentation. First, we construct a graph, in which the elements encode the shot-to-shot coherent characteristics of a video clip based on visual similarity and temporal relation between shots. In this step, we only exploit the inherent property of video itself and it is independent of video genres. Second, spectral method is applied on the graph to group shots into scenes. The proposed method is not only simple but also effective to deal with organized features. Experimental results validate the robustness of our method on different kinds of videos.