27182192
A unified architecture for real-time video-coding systems
This paper presents a unified architecture for a live video over the Internet with emphasis on solving some challenging problems such as network bandwidth adaptation for rate and congestion, loss packet recovery, joint source and channel coding, and packetization. In our architecture, a time-varying bit rate for the source coding and time-varying ratios for the channel coding are simultaneously computed by a new congestion-control protocol. An adaptive rate-control scheme is then proposed to calculate quantization parameters and to determine the number of skipping frames corresponding to the bit rate. An adaptive unequal error-control scheme is also provided to protect the bitstream. Furthermore, a simple and MPEG-4 standard compatible algorithm is designed to packetize generated bitstream at the SyncLayer by using the existing resynchronization marker approach. With the proposed architecture, the coding efficiency and the robustness of the whole system are improved greatly.
11110859
A locally-adaptive algorithm for measuring blocking artifacts in images and videos
Block transform coding is the most popular approach for image and video compression. The objective measurement of blocking artifacts plays an important role in the design, optimization, and assessment of image and video coding systems. This paper presents a new algorithm for measuring blocking artifacts in images and videos. It exhibits unique and useful features: 1) it examines the blocks individually so that it can measure the severity of blocking artifacts locally; 2) it is a one-pass algorithm in the sense that the image needs to be accessed only once; 3) it takes into account the blocking artifacts for high bit rate images and the flatness for the very low bit rate images; 4) the blocking artifacts measure is well-defined in the range of 0-10. Experiments on various still images and videos show that this blockiness measure is very efficient in terms of computational complexity and memory usage, and can produce consistent blocking artifacts measurement.
8588212
Temporal Aggregates in OWL-Time
In this paper, we describe our approach for representing temporal aggregates in OWL-Time, and a systematic way of mapping iCalendar recurrence sets to OWL-Time temporal sequences. We present several examples of natural language expressions for temporal aggregates and how they would be represented in OWL-Time; some examples can be described in both iCalendar and OWL-Time, while others can only be represented in OWL-Time. We also illustrate how we can translate from a natural language sentence to our representation.
9260921
Complexity adaptive quantization for intra-frames in very low bit rate video coding
Conventional rate control schemes focus on the problem of finding an optimal quantization value for P- and B-frames. No rate control is available for the encoding of I-frames. This could pose big problems due to the large number of bits an I-frame could generate, and due to the fact that the number of bits varies drastically from sequence to sequence, depending on their image complexity. This problem becomes severe especially at very low bit rate. Therefore, a mechanism to allocate data bits to an I-frame according to its complexity is indispensable in order to have constant coding quality. This work presents a mechanism to establish for I-frames a generic relationship between the quantization value, data bits, and their image complexity. Experimental results show that this generic relationship provides a fairly accurate estimation of quantization value for an I-frame at given data bits and image complexity, and is very useful in controlling the data bits generated by an I-frame.
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.
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.
14571890
COBBLER: combining column and row enumeration for closed pattern discovery
The problem of mining frequent closed patterns has received considerable attention recently as it promises to have much less redundancy compared to discovering all frequent patterns. Existing algorithms can presently be separated into two groups, feature (column) enumeration and row enumeration. Feature enumeration algorithms like CHARM and CLOSET+ are efficient for datasets with small number of features and large number of rows since the number of feature combinations to be enumerated is small. Row enumeration algorithms like CARPENTER on the other hand are more suitable for datasets (eg. bioinformatics data) with large number of features and small number of rows. Both groups of algorithms, however, will encounter problem for datasets that have large number of rows and features. In this paper, we describe a new algorithm called COBBLER which can efficiently mine such datasets . COBBLER is designed to dynamically switch between feature enumeration and row enumeration depending on the data characteristic in the process of mining. As such, each portion of the dataset can be processed using the most suitable method, making the mining more efficient. Several experiments on real-life and synthetic datasets show that COBBLER is an order of magnitude better than previous closed pattern mining algorithms like 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.
24069960
Study of Improved Hierarchy Genetic Algorithm Based on Adaptive Niches
Canonical genetic algorithms have the defects of pre-maturity and stagnation when applied in optimizing problems. In order to avoid the shortcomings, an adaptive niche hierarchy genetic algorithm (ANHGA) is proposed. The algorithm is based on the adaptive mutation operator and crossover operator to adjust the crossover rate and probability of mutation of each individual, whose mutation values are decided using individual gradient. This approach is applied in Percy and Shubert function optimization. Comparisons of niche genetic algorithm (NGA), hierarchy genetic algorithm (HGA) and ANHGA have been done by establishing a simulation model and the results of mathematics model and actual industrial model show that ANHGA is feasible and efficient in the design of multi-extremum.
2768791
Finding representative set from massive data
In the information age, data is pervasive. In some applications, data explosion is a significant phenomenon. The massive data volume poses challenges to both human users and computers. In this project, we propose a new model for identifying representative set from a large database. A representative set is a special subset of the original dataset, which has three main characteristics: It is significantly smaller in size compared to the original dataset. It captures the most information from the original dataset compared to other subsets of the same size. It has low redundancy among the representatives it contains. We use information-theoretic measures such as mutual information and relative entropy to measure the representativeness of the representative set. We first design a greedy algorithm and then present a heuristic algorithm that delivers much better performance. We run experiments on two real datasets and evaluate the effectiveness of our representative set in terms of coverage and accuracy. The experiments show that our representative set attains expected characteristics and captures information more efficiently.
12021051
COE: A General Approach for Efficient Genome-Wide Two-Locus Epistasis Test in Disease Association Study
The availability of high density single nucleotide polymorphisms (SNPs) data has made genome-wide association study computationally challenging. Two-locus epistasis (gene-gene interaction) detection has attracted great research interest as a promising method for genetic analysis of complex diseases. In this paper, we propose a general approach, COE, for efficient large scale gene-gene interaction analysis, which supports a wide range of tests. In particular, we show that many commonly used statistics are convex functions. From the observed values of the events in two-locus association test, we can develop an upper bound of the test value. Such an upper bound only depends on single-locus test and the genotype of the SNP-pair. We thus group and index SNP-pairs by their genotypes. This indexing structure can benefit the computation of all convex statistics. Utilizing the upper bound and the indexing structure, we can prune most of the SNP-pairs without compromising the optimality of the result. Our approach is especially efficient for large permutation test. Extensive experiments demonstrate that our approach provides orders of magnitude performance improvement over the brute force approach.