18071478
A perceptual bit allocation scheme for H.264
This paper aims at improving perceptual quality of encoded video sequences, and proposes a new perceptual bit allocation scheme for H.264. Firstly, a new motion complexity measure is defined to represent the amount of motion contents between two consecutive frames, and is used to estimate the target bit at frame level. Secondly, a segmentation method exploiting the perceptual characteristics of the video contents is presented, and is used to adaptively update the Lagrangian multiplier in the coding mode selection at macroblock level. Thirdly, based on the motion complexity and Lagrangian multiplier updating, a rate control scheme for H.264 is proposed. Experimental results show that our scheme can effectively improve the perceptual video quality as compared with the H.264 adopted rate control algorithm. Moreover, our scheme also achieves an average peak signal-to-noise ratio gain of up to 0.138 dB for the test sequences.
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.
20260133
Temporal aggregates for Web services on the semantic Web
In this paper we describe how we encode our temporal aggregates ontology in OWL for Web services on the semantic Web. We also present one example to show how to use the ontology to represent temporal aggregates information.
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.
981043
Learning Event Durations from Event Descriptions
We have constructed a corpus of news articles in which events are annotated for estimated bounds on their duration. Here we describe a method for measuring inter-annotator agreement for these event duration distributions. We then show that machine learning techniques applied to this data yield coarse-grained event duration information, considerably outperforming a baseline and approaching human performance.
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.
3069611
A General Framework for Fast Co-clustering on Large Datasets Using Matrix Decomposition
Simultaneously clustering columns and rows (co- clustering) of large data matrix is an important problem with wide applications, such as document mining, microarray analysis, and recommendation systems. Several co-clustering algorithms have been shown effective in discovering hidden clustering structures in the data matrix. For a data matrix of m rows and n columns, the time complexity of these methods is usually in the order of mtimesn (if not higher). This limits their applicability to data matrices involving a large number of columns and rows. Moreover, an implicit assumption made by existing co-clustering methods is that the whole data matrix needs to be held in the main memory. In this paper, we propose a general framework, CRD, for co-clustering large datasets utilizing recently developed sampling- based matrix decomposition methods. The time complexity of our approach is linear in m and n. And it does not require the whole data matrix be in the main memory. Experimental results show that CRD achieves competitive accuracy to existing co-clustering methods but with much less computational cost.
16446501
Mining frequent closed patterns in microarray data
Microarray data typically contains a large number of columns and a small number of rows, which poses a great challenge for existing frequent (closed) pattern mining algorithms that discover patterns in item enumeration space. In this paper, we propose two algorithms that explore the row enumeration space to mine frequent closed patterns. Several experiments on real-life gene expression data show that the algorithms are faster than existing algorithms, including CLOSET, CHARM, CLOSET+ and CARPENTER.
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.
3409646
A survey of passive technology for digital image forensics
Over the past years, digital images have been widely used in the Internet and other applications. Whilst image processing techniques are developing at a rapid speed, tampering with digital images without leaving any obvious traces becomes easier and easier. This may give rise to some problems such as image authentication. A new passive technology for image forensics has evolved quickly during the last few years. Unlike the signature-based or watermark-based methods, the new technology does not need any signature generated or watermark embedded in advance. It assumes that different imaging devices or processing would introduce different inherent patterns into the output images. These underlying patterns are consistent in the original untampered images and would be altered after some kind of manipulations. Thus, they can be used as evidence for image source identification and alteration detection. In this paper, we will discuss this new forensics technology and give an overview of the prior literatures. Some concluding remarks are made about the state of the art and the challenges in this novel technology.
16312564
A new hybrid model for time-series prediction
This paper proposed a new hybrid model in order to increase time series prediction accuracy. This hybrid model considers the routine time prediction technique like AR, ANN or any others as atomic building block. A linear hybrid technique is used to combine their forecast result into the final result. The hybrid algorithm was tested against three different kinds of time series data. Experiments results showed the effectiveness of the proposed hybrid model.