Modeling and Dimensioning Hierarchical Storage Systems for Low-Delay Video Services
In order to cost-effectively accommodate a large number of titles in a video system, a hierarchical storage system can be used. In this system, not-so-popular video files are stored in a tertiary level such as a disk/tape library. These files are transferred, or "staged," to a secondary level composed of magnetic disks before being streamed to the users. This system overcomes the current limitations in using disk/tape libraries to stream videos and resolves the bandwidth difference between staging and streaming. We present, via analysis, a model of the system and determine the minimum storage and bandwidth required, at each level, to meet a given user delay goal. We also analyze a number of system operations pertaining to whether or not a file is played while it is being staged (i.e., stage-streaming) and whether or not the displayed segments are deleted (i.e., trail-deletion). We show that stage-streaming and trail-deletion can achieve substantially lower bandwidth and storage requirements. In order to further increase the streaming and storage scalability, a distributed storage system can be used where multiple local servers are put close to user pools and get their files from one of the libraries through a network. We extend the models developed to such a system and specify the resource requirements to meet a given delay goal.

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: 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: Fine-grained device management in an interactive media server
abstract: 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: 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: Sentiment-based search in digital libraries
abstract: Several researchers have developed tools for classifying/ clustering Web search results into different topic areas (such as sports, movies, travel, etc.), and to help users identify relevant results quickly in the area of interest. This study follows a similar approach, but is in the area of sentiment classification - automatically classifying on-line review documents according to the overall sentiment expressed in them. This paper presents a prototype system that has been developed to perform sentiment categorization of Web search results. It assists users to quickly focus on recommended (or non-recommended) information by classifying Web search results into four categories: positive, negative, neutral, and non-review documents, by using an automatic classifier based on a supervised machine learning algorithm, support vector machine (SVM)

title: On the use of hierarchical information in sequential mining-based XML document similarity computation
abstract: Measuring the structural similarity among XML documents is the task of finding their semantic correspondence and is fundamental to many web-based applications. While there exist several methods to address the problem, the data mining approach seems to be a novel, interesting and promising one. It explores the idea of extracting paths from XML documents, encoding them as sequences and finding the maximal frequent sequences using the sequential pattern mining algorithms. In view of the deficiencies encountered by ignoring the hierarchical information in encoding the paths for mining, a new sequential pattern mining scheme for XML document similarity computation is proposed in this paper. It makes use of a preorder tree representation (PTR) to encode the XML tree’s paths so that both the semantics of the elements and the hierarchical structure of the document can be taken into account when computing the structural similarity among documents. In addition, it proposes a postprocessing step to reuse the mined patterns to estimate the similarity of unmatched elements so that another metric to qualify the similarity between XML documents can be introduced. Encouraging experimental results were obtained and reported.

title: Hypertext-assisted video indexing and content-based retrieval
abstract: An effective approach has been designed for the construction of on-line educational video systems. We have made use of the assistance of HTML text for accurate video parsing and complete content extraction. A video system with segmentation/indexing module and browsing/query modules are implemented to demonstrate the idea.

title: Semantic Analysis of Basketball Video Using Motion Information
abstract: This paper presents a new method for extracting semantic information from basketball video. Our approach consists of three stages: shot and scene boundary detection, scene classification and semantic video analysis for event detection. The scene boundary detection algorithm is based on both visual and motion prediction information. After the shot and scene boundary detection, a set of visual and motion features are extracted from scene or shot. The motion features, describing the total motion, camera motion and object motion within the scene respectively, are computed from the motion vector of the compressed video using an iterative algorithm with robust outlier rejection. Finally, the extracted features are used to differentiate offensive/defensive activities in the scenes. By analyzing the offensive/defensive activities, the positions of potential semantic events, such as foul and goal, are located. Experimental results demonstrate the effectiveness of the proposed method.

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.