Fine-grained device management in an interactive media server
The use of interactive media has already gained considerable popularity. Interactivity gives viewers VCR controls like slow-motion, pause, fast-forward, and instant replay. However, traditional server-based or client-based approaches for supporting interactivity either consume too much network bandwidth or require large client buffering; and hence they are economically unattractive. We propose the architecture and design of an interactive media proxy (IMP) server that transforms noninteractive broadcast or multicast streams into interactive ones for servicing a large number of end users. For IMP to work cost-effectively, it must carefully manage its storage devices, which are needed for caching voluminous media data. In this regard, we propose a fine-grained device management strategy consisting of three complementary components: disk profiler, data placement, and IO scheduler. Through quantitative analysis and experiments, we show that these fine-grained strategies considerably improve device throughput under various workload scenarios.

title: Distributed servers architecture for networked video services
abstract: In an on-demand video system, the video repository generally has limited streaming capacities and may be far from the users. In order to achieve higher user capacity and lower network transmission cost, distributed servers architecture can be used, in which multiple local servers are placed close to user pools and, according to their local demands, dynamically cache the contents streamed from the repository. We study a number of caching schemes as applied in the local servers depending on whether the repository is able to multicast movie contents to the local servers or not, and whether the local servers can exchange their cached contents among themselves or not. Our caching schemes keep a circular buffer of data for the movie requested, and hence movies are partially cached. By adjusting the size of the buffer, such caching is able to achieve better tradeoff between network channels and local storage as compared to the traditional caching in which a movie is treated as an entity. For each caching scheme, we study the tradeoff between the local storage and the network channels, and address how the total cost of the system can be minimized by appropriately sizing the buffer. As compared to a number of traditional operations (request batching and multicasting, true-VOD, etc.), we show that distributed servers architecture is able to achieve much lower system cost to offer on-demand video services.

title: Improved refinement search for H.263 to H.264/AVC transcoding based on the minimum cost tendency search
abstract: An improved refinement search method for transcoding from H.263 to H.264/AVC is proposed in this paper. Many existing motion re-estimation methods refine the input motion vector (MV) with a small search range, which is usually input MV biased. Motion estimation (ME) in H.263 usually does not consider the rate required for coding the MV, and hence, the input MV may incur a large cost in H.264/AVC. To overcome this problem, we introduce a refinement search method, called minimum cost tendency search (MCTS), which takes the difference between the cost functions for ME in H.263 and H.264/AVC into consideration. The input MV and the predictor MV are used as two anchor points. The proposed MCTS starts searching from the anchor point with a higher cost to another. Finally, the best point is chosen as the center for further refinement. The performance of MCTS is evaluated by comparing with full search, FME in JM software and refinement scheme using small diamond pattern around the input MV (RSD). Experimental results show the proposed MCTS performs more stable than FME and RSD over a wide range of output video quality

title: Low-Complexity Rate Control for Efficient H.263 to H.264/AVC Video Transcoding
abstract: Rate control is a complicated problem in the H.264/AVC coding standard, extra computation is usually needed for the existing rate control schemes to estimate the complexity of frames or macroblocks (MBs). However, during transcoding, information from preceded video could be used to simplify the rate control. In this paper, we propose a low-complexity rate control scheme for transcoding from H.263 to H.264/AVC. The relationship between the rate of the pre-coded video and both the rate and distortion of the transcoded video are studied. By using only the rate information from the preceded video, we introduce a row-layer bit allocation and perform average rate shaping across a row of MBs. Estimation error diffusion is also introduced. The proposed scheme has sufficiently lower computational complexity than other methods as there is no explicit complexity measurement of MBs and complicated parameters updating. Experimental results show the effectiveness of the proposed scheme.

title: A new motion compensation approach for error resilient video coding
abstract: Multihypothesis motion-compensated prediction (MHMCP) can be used as an error resilience technique for video coding. Motivated by MHMCP, we propose a new error resilience approach named alternative motion-compensated prediction (AMCP), where two-hypothesis and one-hypothesis predictions are alternatively used with some mechanism. Both theory and simulation results show that in case of one frame loss, the expected converged error using AMCP is smaller than that using two-hypothesis MCP.

title: A Cost-Based Evaluation of End-to-End Network Measurements in Overlay Multicast
abstract: Application-layer multicast (ALM, or overlay multicast) has been proposed to overcome limitations in IP multicast. While much measurement work (such as delay or connectivity measurement) has been conducted to build efficient ALM trees, several interesting questions therein are left unclear: (1) What are the measurement costs of the different measurement methods? (2) What is the major improvement to an ALM tree by using a certain measurement method? (3) To achieve the target tree performance, what is the most cost-efficient measurement method? In this paper, we study three representative measurement methods, i.e., delay measurement, connectivity measurement and available bandwidth measurement. We select six typical ALM protocols each adopting at least one of the measurement methods and evaluate their performance on Internet-like topologies. Our study shows that delay and connectivity measurements can effectively reduce end-to-end delay in overlay trees with low measurement costs. Only using delay measurement may lead to a tree consuming much network resource. The use of connectivity and bandwidth measurements can build a tree with low resource consumption.

title: A collaborative filtering framework based on fuzzy association rules and multiple-level similarity
abstract: The rapid development of Internet technologies in recent decades has imposed a heavy information burden on users. This has led to the popularity of recommender systems, which provide advice to users about items they may like to examine. Collaborative Filtering (CF) is the most promising technique in recommender systems, providing personalized recommendations to users based on their previously expressed preferences and those of other similar users. This paper introduces a CF framework based on Fuzzy Association Rules and Multiple-level Similarity (FARAMS).  extended existing techniques by using fuzzy association rule mining, and takes advantage of product similarities in taxonomies to address data sparseness and nontransitive associations. Experimental results show that FARAMS improves prediction quality, as compared to similar approaches.

title: Query Expansion by Raw Image Features and Text Annotations in Image Retrieval
abstract: In this paper, we investigate the effect of using several color and texture features in query expansion. We assume that the initial queries are text-based and are matched against the text annotations. The system performs a two-pass retrieval by expanding the initial query using text and image features from the initial retrieved set, assuming that it contains more relevant images than non-relevant ones. We performed extensive experiments using 1019 color pictures with captions, relevance judgments and queries supplied by a national archives agency.

title: Mapping XML Schema to Relations Using Genetic Algorithm
abstract: As web-applications grow in number and complexity, there is a need for efficient mappings from XML schemas to the flat relational tables so that existing functions in relational database systems can be utilized. However, many of the existing mapping methods, such as the model-based or the structure-based methods, do not exploit query history for better query performance. In this paper, we propose the use of genetic algorithm (GA) in a cost-based approach for converting a XML schema to relational tables. By formulating the mapping problem as a cost optimization task with respect to a set of weighted frequent queries, we can obtain an efficient mapping that minimizes the queries execution time. In our experiments, we show that the mapping obtained by GA is superior to other non-cost-based approaches. In particular, the GA approach has out-performed the greedy heuristic in the browsing queries where the accessed attributes are many and scattered.

title: Ontology for Nature-Scene Image Retrieval
abstract: This paper presents a framework for building an ontology to provide semantic interpretations in image contents. The novelty of this framework comes from building a MPEG-7 ontology for semantic representations of multimedia contents, and from integrating such ontology into an image retrieval system to enable fast, efficient image query and retrieval. The prototype system demonstrated the feasibility of embedding such ontology into an image retrieval system. Its main objective has been achieved by retrieving nature scene images using human readable keywords. Based on the experimental results, we believe that using our ‘bridging’ technique, the high-level non-machine readable human concepts can be seamlessly mapped to low-level machine processable data. This helps to improve the efficiency of our CBIR system compared to conventional methods.

title: Extracting Causal Knowledge from a Medical Database Using Graphical Patterns
abstract: This paper reports the first part of a project that aims to develop a knowledge extraction and knowledge discovery system that extracts causal knowledge from textual databases. In this initial study, we develop a method to identify and extract cause-effect information that is explicitly expressed in medical abstracts in the Medline database. A set of graphical patterns were constructed that indicate the presence of a causal relation in sentences, and which part of the sentence represents the cause and which part represents the effect. The patterns are matched with the syntactic parse trees of sentences, and the parts of the parse tree that match with the slots in the patterns are extracted as the cause or the effect.