39269573
An adaptive edge-preserving artifacts removal filter for video post-processing
We propose an adaptive edge-preserving artifacts removal filter (EPARF) for post-processing video compressed using block-based motion compensation so as to improve visual quality. The proposed filter is based on half-quadratic regularization, and it can adaptively remove both blocking and ringing artifacts while preserving object edges. The proposed algorithm has been applied to video sequences compressed by the MSRA scalable video coding (SVC) codec with block-based motion-compensated temporal filtering (MCTF). Experimental results demonstrate that EPARF can significantly improve both subjective and objective quality.
9886444
Computing Semantic Similarity between Skill Statements for Approximate Matching
This paper explores the problem of computing text similarity between verb phrases describing skilled human behavior for the purpose of finding approximate matches. Four parsers are evaluated on a large corpus of skill statements extracted from an enterprise-wide expertise taxonomy. A similarity measure utilizing common semantic role features extracted from parse trees was found superior to an information-theoretic measure of similarity and comparable to the level of human agreement.
22420647
Adaptive intra-frame quantization for very low bit rate video coding
This paper presents, for I-frames, a generic relationship among the quantization value, data bits, and their spatial complexity. This relationship provides a mechanism to select the quantization value for an I-frame (with different spatial complexity) at given bit budget before actually encoding it. Therefore it helps to adaptively distribute the bit budget among I-, P- and B-frames in a sequence. Experiments show that the adaptive intra frame quantization algorithm reduces the number of frame skippings significantly with comparable average PSNRs, and thus improves the overall subjective quality of the reconstructed video.
7351167
Fast intermode decision in H.264/AVC video coding
The new video coding standard, H.264/MPEG-4 AVC, uses variable block sizes ranging from 4/spl times/4 to 16/spl times/16 in interframe coding. This new feature has achieved significant coding gain compared to coding a macroblock (MB) using fixed block size. However, this feature results in extremely high computational complexity when brute force rate distortion optimization (RDO) algorithm is used. This paper proposes a fast intermode decision algorithm to decide the best mode in intercoding. It makes use of the spatial homogeneity and the temporal stationarity characteristics of video objects. Specifically, spatial homogeneity of a MB is decided based on the MB's edge intensity, and temporal stationarity is decided by the difference of the current MB and it colocated counterpart in the reference frame. Based on the homogeneity and stationarity of the video objects, only a small number of intermodes are selected in the RDO process. The experimental results show that the fast intermode decision algorithm is able to reduce on the average 30% encoding time, with a negligible peak signal-to-noise ratio loss of 0.03 dB or, equivalently, a bit rate increment of 0.6%.
13046627
Adaptive frame skipping based on spatio-temporal complexity for low bit-rate video coding
Abstract Conventional rate control schemes focus on the adjustment of quantization values to retain a certain buffer level, and buffer adaptive frame skippings are used to avoid buffer overflow at low bit-rates. In this paper, an adaptive frame-skipping scheme is proposed to achieve the balance between spatial and temporal quality, and to maintain the buffer level. The occurrence of frame skipping is jointly dependent on the temporal and spatial complexity of the video, and on the fullness of the buffer. This helps to achieve a balanced spatial and temporal quality and to enhance the overall perceptual quality. Experimental results show that the new scheme is simple but very effective, with large average PSNR gains and consistently improved visual quality, and the improvement in perceptual quality is much significant than that in average PSNRs.
11419332
An Optimal Controller Based-on GA for Direct Torque Control
In direct torque control (DTC) of induction motor, the performance of system is influenced by the given value of torque. The given torque is the output of speed regulator. And PID adjustor is used generally as the speed regulator, so the selection of PID parameters is crucial to DTC system. In this paper, a new optimal control strategy of DTC based-on genetic algorithm (GA) for induction motor is presented. In this method, GA is used to optimize the PID parameters of speed regulator, so the precision of given torque is improved, and the dynamic response of torque is more satisfying than traditional DTC. The results of simulation indicate the validity of this method.
41075889
Efficient Compression of Multi-view Video Using Hierarchical B Pictures
3D and free viewpoint video are new types of natural video media that expand the user's sensation far beyond what is offered by traditional media. As video compression is a key issue in traditional video communications and the use of multi video streams makes it even more important for such applications. In this paper, we presented an investigation of the multi- view video coding structures with focus on exploiting hierarchical B pictures. We demonstrate hierarchical B pictures prediction structures, and present the advantage of several multi-view video coding prediction schemes. This demonstration will show the benefits of using hierarchical B pictures based on H.264/AVC. The simulation results demonstrate that the objective quality can be improved with the advanced MVC prediction structures in comparison to the simulcast coding structures.
207290821
Discriminating between photorealistic computer graphics and natural images using fractal geometry
Rendering technology in computer graphics (CG) is now capable of producing highly photorealistic images, giving rise to the problem of how to identify CG images from natural images. Some methods were proposed to solve this problem. In this paper, we give a novel method from a new point of view of image perception. Although the photorealistic CG images are very similar to natural images, they are surrealistic and smoother than natural images, thus leading to the difference in perception. A part of features are derived from fractal dimension to capture the difference in color perception between CG images and natural images, and several generalized dimensions are used as the rest features to capture difference in coarseness. The effect of these features is verified by experiments. The average accuracy is over 91.2%.
2696556
Dynamic Modeling of Biotechnical Process Based on Online Support Vector Machine
Due to the complexity and high non-linearity of biotechnical process, most simple mathematical models cannot describe the behavior of biochemistry systems very well. Therefore, dynamic modeling of biotechnical process is indispensable. Support vector machine (SVM) is a novel machine learning method, which is powerful for the problem characterized by small sample, non-linearity, high dimension and local minima, and has high generalization. But currently most support vector machine regression (SVR) training algorithms are offline, which could not be suit for time-variant system. So an improved SVM called online support vector machine was presented to modeling for the dynamic feature of fermentation process. The model based on the modified SVM was developed and demonstrated using simulation experiments. Some models based on SVM were also presented. The result shows that the modeling based online SVM is superior to modeling based on SVW.
18783473
A multi-objective-based non-stationary UAV assignment model for constraints handling using PSO
An unmanned aerial vehicle (UAV) assignment requires allocating vehicles to destinations to complete various jobs. It is a complex assignment problem with hard constraints, and potential dimensional explosion when the scenarios become more complicated and the size of problems increases. Moreover, the non-stationary UAV assignment problem, studied in the paper, is more difficult, since dynamic scenarios are introduced, e.g. change of the number, or different task requirements of targets and vehicle, etc. In this paper, a "Constraint-First-Objective- Next" model is proposed for the non-stationary problem. The proposed model can effectively handle constraints as an additional objective, including constraints expressed by nature language, and is flexible enough to be combined with kinds of intelligent computation algorithms. A local version of PSO is cooperated with the proposed model to solve non-stationary UAV assignment problems. Numerical experimental results illustrate that it can efficiently achieve the optima and demonstrate the effectiveness.