Low-Complexity Rate Control for Efficient H.263 to H.264/AVC Video Transcoding
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: Video loss recovery with FEC and stream replication
abstract: Packet loss is inevitable in video multicast. In this paper, we propose and study an effective feedback-free loss recovery scheme for layered video which combines forward error correction (FEC) and stream replication. In our scheme, the server multicasts the video in parallel with FEC packets and a number of replicated delayed (ReD) version of the stream. Receivers autonomously and dynamically join the FEC and ReD streams to repair their losses. On the server side, we analyze and optimize the number of replicated streams and FEC packets to meet a certain residual loss requirement (i.e., error after correction). On the receiver side, we analyze the optimal combination of FEC and ReD packets to minimize its loss. We also present a fast yet accurate approximation algorithm for receiver to make such decision. We show that FEC combined with merely one or two replicated streams can effectively reduce the residual error rate (by as much as 50%) as compared with pure FEC or replication alone. Both subjective and objective video measures confirm that our recovery scheme achieves much better visual quality.

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: 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: 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: Token-tray/weighted queuing-time (TT/WQT): an adaptive batching policy for near video-on-demand system
abstract: In near video-on-demand (near-VoD), requests for a video title are grouped together (i.e. batched) and are served with a single multicast stream, thereby increasing the number of concurrent users which can be supported by the system. Since users may not be able to tolerate the delay incurred by batching and hence cancel their requests, a batching policy should be designed so as to achieve low user loss and high revenue (given by the total pay-per-view collected over a long period of time across all movies). We propose an adaptive batching policy which offers users low delay at low arrival rate, and gates the allocation of the channels at high rate. Such adaptivity is achieved by the use of a simple "token-tray" (TT) scheme which governs when a stream may be allocated to a movie. In assigning a movie to a stream, we propose a weight function which depends on the user queuing-time and its pay-per-view (hence the term "weighted queuing-time") (WQT). By comparing our batching policy (TT/WQT) with a number of traditional ones (FCFS, forced-wait, batch-size-based scheme, etc.), our scheme is shown to achieve the highest revenue and lowest loss rate even when the arrival rate changes, with the user loss rate across the movies being fairly uniform, and the user delay being fairly low even at high arrival rate.

title: Accessing Hidden Web Documents by Metasearching a Directory of Specialty Search Engines
abstract: Many valuable Web documents have not been indexed by general search engines and are only accessible through specific search interfaces. Metasearching groups of specialty search engines is one possible way to gain access to large amount of such hidden Web resources. One of the key issues for returning quality metasearch results is how to select the most relevant specialty search engines for a given query. We introduce a method for categorizing specialty search engines automatically into a hierarchical directory for metasearching. By utilizing the directory, specialty search engines that have a high possibility of having relevant information and resources can be easily selected by a metasearch engine. We evaluate our algorithm by comparing the directory built by the proposed algorithm with another one that was built by human-judgments. In addition, we present a metasearch engine prototype, which demonstrates that such a specialty search engine directory can be beneficial in locating essential but hidden Web resources.

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: 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: 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.