Failure recovery algorithms for multimedia servers

In this paper, we present two novel disk failure recovery methods that utilize the inherent characteristics of video streams for efficient recovery. Whereas the first method exploits the inherent redundancy in video streams (rather than error-correcting codes) to approximately reconstruct data stored on failed disks, the second method exploits the sequentiality of video playback to reduce the overhead of online failure recovery in conventional RAID arrays. For the former approach, we present loss-resilient versions of JPEG and MPEG compression algorithms. We present an inherently redundant array of disks (IRAD) architecture that combines these loss-resilient compression algorithms with techniques for efficient placement of video streams on disk arrays to ensure that on-the-fly recovery does not impose any additional load on the array. Together, they enhance the scalability of multimedia servers by (1) integrating the recovery process with the decompression of video streams, and thereby distributing the reconstruction process across the clients; and (2) supporting graceful degradation in the quality of recovered images with increase in the number of disk failures. We present analytical and experimental results to show that both schemes significantly reduce the failure recovery overhead in a multimedia server.     

Scene change detection techniques for video database systems

Scene change detection (SCD) is one of several fundamental problems in the design of a video database management system (VDBMS). It is the first step towards the automatic segmentation, annotation, and indexing of video data. SCD is also used in other aspects of VDBMS, e.g., hierarchical representation and efficient browsing of the video data. In this paper, we provide a taxonomy that classifies existing SCD algorithms into three categories: full-video-image-based, compressed-video-based, and model-based algorithms. The capabilities and limitations of the SCD algorithms are discussed in detail. The paper also proposes a set of criteria for measuring and comparing the performance of various SCD algorithms. We conclude by discussing some important research directions.     

Automatic text segmentation and text recognition for video indexing

Efficient indexing and retrieval of digital video is an important function of video databases. One powerful index for retrieval is the text appearing in them. It enables content-based browsing. We present our new methods for automatic segmentation of text in digital videos. The algorithms we propose make use of typical characteristics of text in videos in order to enable and enhance segmentation performance. The unique features of our approach are the tracking of characters and words over their complete duration of occurrence in a video and the integration of the multiple bitmaps of a character over time into a single bitmap. The output of the text segmentation step is then directly passed to a standard OCR software package in order to translate the segmented text into ASCII. Also, a straightforward indexing and retrieval scheme is introduced. It is used in the experiments to demonstrate that the proposed text segmentation algorithms together with existing text recognition algorithms are suitable for indexing and retrieval of relevant video sequences in and from a video database. Our experimental results are very encouraging and suggest that these algorithms can be used in video retrieval applications as well as to recognize higher level semantics in videos.     

Automatic video data structuring through shot partitioning and key-frame computing

In video processing, a common first step is to segment the videos into physical units, generally called shots. A shot is a video segment that consists of one continuous action. In general, these physical units need to be clustered to form more semantically significant units, such as scenes, sequences, programs, etc. This is the so-called story-based video structuring. Automatic video structuring is of great importance for video browsing and retrieval. The shots or scenes are usually described by one or several representative frames, called key-frames. Viewed from a higher level, key frames of some shots might be redundant in terms of semantics. In this paper, we propose automatic solutions to the problems of: (i) video partitioning, (ii) key frame computing, (iii) key frame pruning. For the first problem, an algorithm called “net comparison” is devised. It is accurate and fast because it uses both statistical and spatial information in an image and does not have to process the entire image. For the last two problems, we develop an original image similarity criterion, which considers both spatial layout and detail content in an image. For this purpose, coefficients of wavelet decomposition are used to derive parameter vectors accounting for the above two aspects. The parameters exhibit (quasi-) invariant properties, thus making the algorithm robust for many types of object/camera motions and scaling variances. The novel “seek and spread” strategy used in key frame computing allows us to obtain a large representative range for the key frames. Inter-shot redundancy of the key-frames is suppressed using the same image similarity measure. Experimental results demonstrate the effectiveness and efficiency of our techniques.     

Zodiac: A history-based interactive video authoring system

Easy-to-use audio/video authoring tools play a crucial role in moving multimedia software from research curiosity to mainstream applications. However, research in multimedia authoring systems has rarely been documented in the literature. This paper describes the design and implementation of an interactive video authoring system called Zodiac, which employs an innovative edit history abstraction to support several unique editing features not found in existing commercial and research video editing systems. Zodiac provides users a conceptually clean and semantically powerful branching history model of edit operations to organize the authoring process, and to navigate among versions of authored documents. In addition, by analyzing the edit history, Zodiac is able to reliably detect a composed video stream's shot and scene boundaries, which facilitates interactive video browsing. Zodiac also features a video object annotation capability that allows users to associate annotations to moving objects in a video sequence. The annotations themselves could be text, image, audio, or video. Zodiac is built on top of MMFS, a file system specifically designed for interactive multimedia development environments, and implements an internal buffer manager that supports transparent lossless compression/decompression. Shot/scene detection, video object annotation, and buffer management all exploit the edit history information for performance optimization.     

Constructing table-of-content for videos

A fundamental task in video analysis is to extract structures from the video to facilitate user's access (browsing and retrieval). Motivated by the important role that the table of content (ToC) plays in a book, in this paper, we introduce the concept of ToC in the video domain. Some existing approaches implicitly use the ToC, but are mainly limited to low-level entities (e.g., shots and key frames). The drawbacks are that low-level structures (1) contain too many entries to be efficiently presented to the user; and (2) do not capture the underlying semantic structure of the video based on which the user may wish to browse/retrieve. To address these limitations, in this paper, we present an effective semantic-level ToC construction technique based on intelligent unsupervised clustering. It has the characteristics of better modeling the time locality and scene structure. Experiments based on real-world movie videos validate the effectiveness of the proposed approach. Examples are given to demonstrate the usage of the scene-based ToC in facilitating user's access to the video.     

Searching and editing MPEG-compressed video in a distributed online environment

WebClip (on-line demo at http://www.ctr.columbia.edu/webclip) is a compressed video searching and editing system operating over the World Wide Web. WebClip uses a distributed client-server model including a server engine for content analysis/editing, and clients for interactive controls of video browsing/editing. It specializes several unique features, including compressed-domain video feature extraction and manipulation, multi-resolution video access, content based video browsing/retrieval, and a distributed network architecture.     

I/O scheduling for digital continuous media

A growing set of applications require access to digital video and audio. In order to provide playback of such continuous media (CM), scheduling strategies for CM data servers (CMS) are necessary. In some domains, particularly defense and industrial process control, the timing requirements of these applications are strict and essential to their correct operation. In this paper we develop a scheduling strategy for multiple access to a CMS such that the timing guarantees are maintained at all times. First, we develop a scheduling strategy for the steady state, i.e., when there are no changes in playback rate or operation. We derive an optimal Batched SCAN (BSCAN) algorithm that requires minimum buffer space to schedule concurrent accesses. The scheduling strategy incorporates two key constraints: (1) data fetches from the storage system are assumed to be in integral multiples of the block size, and (2) playback guarantees are ensured for frame-oriented streams when each frame can span multiple blocks. We discuss modifications to the scheduling strategy to handle compressed data like motion-JPEG and MPEG. Second, we develop techniques to handle dynamic changes brought about by VCR-like operations executed by applications. We define a suite of primitive VCR-like operations that can be executed. We show that an unregulated change in the BSCAN schedule, in response to VCR-like operations, will affect playback guarantees. We develop two general techniques to ensure playback guarantees while responding to VCR-like operations: passive and active accumulation. Using user response time as a metric we show that active accumulation algorithms outperform passive accumulation algorithms. An optimal response-time algorithm in a class of active accumulation strategies is derived. The results presented here are validated by extensive simulation studies.     

Approximate query processing using wavelets

Approximate query processing has emerged as a cost-effective approach for dealing with the huge data volumes and stringent response-time requirements of today's decision support systems (DSS). Most work in this area, however, has so far been limited in its query processing scope, typically focusing on specific forms of aggregate queries. Furthermore, conventional approaches based on sampling or histograms appear to be inherently limited when it comes to approximating the results of complex queries over high-dimensional DSS data sets. In this paper, we propose the use of multi-dimensional wavelets as an effective tool for general-purpose approximate query processing in modern, high-dimensional applications. Our approach is based on building wavelet-coefficient synopses of the data and using these synopses to provide approximate answers to queries. We develop novel query processing algorithms that operate directly on the wavelet-coefficient synopses of relational tables, allowing us to process arbitrarily complex queries entirely in the wavelet-coefficient domain. This guarantees extremely fast response times since our approximate query execution engine can do the bulk of its processing over compact sets of wavelet coefficients, essentially postponing the expansion into relational tuples until the end-result of the query. We also propose a novel wavelet decomposition algorithm that can build these synopses in an I/O-efficient manner. Finally, we conduct an extensive experimental study with synthetic as well as real-life data sets to determine the effectiveness of our wavelet-based approach compared to sampling and histograms. Our results demonstrate that our techniques: (1) provide approximate answers of better quality than either sampling or histograms; (2) offer query execution-time speedups of more than two orders of magnitude; and (3) guarantee extremely fast synopsis construction times that scale linearly with the size of the data. Received: 7 August 2000 / Accepted: 1 April 2001 Published online: 7 June 2001     

Traffic characterization algorithms for VBR video in multimedia networks

A network that offers deterministic, i.e., worst case, quality-of-service guarantees to variable-bit-rate (VBR) video must provide a resource reservation mechanism that allocates bandwidth, buffer space, and other resources for each video stream. Such a resource reservation scheme must be carefully designed, otherwise network resources are wasted. A key component for the design of a resource reservation scheme is the traffic characterization method that specifies the traffic arrivals on a video stream. The traffic characterization should accurately describe the actual arrivals, so that a large number of streams can be supported; but it must also map directly into efficient traffic-policing mechanisms that monitor arrivals on each stream. In this study, we present a fast and accurate traffic characterization method for stored VBR video in networks with a deterministic service. We use this approximation to obtain a traffic characterization that can be efficiently policed by a small number of leaky buckets. We present a case study where we apply our characterization method to networks that employ a dynamic resource reservation scheme with renegotiation. We use traces from a set of 25–30-min MPEG sequences to evaluate our method against other characterization schemes from the literature.     

Wavelet-based video indexing and querying

We present several algorithms suitable for analysis of broadcast video. First, we show how wavelet analysis of frames of video can be used to detect transitions between shots in a video stream, thereby dividing the stream into segments. Next we describe how each segment can be inserted into a video database using an indexing scheme that involves a wavelet-based “signature.” Finally, we show that during a subsequent broadcast of a similar or identical video clip, the segment can be found in the database by quickly searching for the relevant signature. The method is robust against noise and typical variations in the video stream, even global changes in brightness that can fool histogram-based techniques. In the paper, we compare experimentally our shot transition mechanism to a color histogram implementation, and also evaluate the effectiveness of our database-searching scheme. Our algorithms are very efficient and run in realtime on a desktop computer. We describe how this technology could be employed to construct a “smart VCR” that was capable of alerting the viewer to the beginning of a specific program or identifying     

Browsing and placement of multi-resolution images on parallel disks

Abstract. With rapid advances in computer and communication technologies, there is an increasing demand to build and maintain large image repositories. To reduce the demands on I/O and network resources, multi-resolution representations are being proposed for the storage organization of images. Image decomposition techniques such as wavelets can be used to provide these multi-resolution images. The original image is represented by several coefficients, one of them with visual similarity to the original image, but at a lower resolution. These visually similar coefficients can be thought of as thumbnails or icons of the original image. This paper addresses the problem of storing these multi-resolution coefficients on disks so that thumbnail browsing as well as image reconstruction can be performed efficiently. Several strategies are evaluated to store the image coefficients on parallel disks. These strategies can be classified into two broad classes, depending on whether the access pattern of the images is used in the placement. Disk simulation is used to evaluate the performance of these strategies. Simulation results are validated with results from experiments with real Disks, and are found to be in good qualitative agreement. The results indicate that significant performance improvements can be achieved with as few as four disks by placing image coefficients based upon browsing access patterns. Work supported by a research grant from NSF/ARPA/NASA IRI9411330 and NSF instrumentation grant CDA-9421978 and NSF Career grant No. IIS-9985019, and NSF grant 0010044-CCR.     

InsightVideo: toward hierarchical video content organization for efficient browsing, summarization and retrieval

Hierarchical video browsing and feature-based video retrieval are two standard methods for accessing video content. Very little research, however, has addressed the benefits of integrating these two methods for more effective and efficient video content access. In this paper, we introduce InsightVideo, a video analysis and retrieval system, which joins video content hierarchy, hierarchical browsing and retrieval for efficient video access. We propose several video processing techniques to organize the content hierarchy of the video. We first apply a camera motion classification and key-frame extraction strategy that operates in the compressed domain to extract video features. Then, shot grouping, scene detection and pairwise scene clustering strategies are applied to construct the video content hierarchy. We introduce a video similarity evaluation scheme at different levels (key-frame, shot, group, scene, and video.) By integrating the video content hierarchy and the video similarity evaluation scheme, hierarchical video browsing and retrieval are seamlessly integrated for efficient content access. We construct a progressive video retrieval scheme to refine user queries through the interactions of browsing and retrieval. Experimental results and comparisons of camera motion classification, key-frame extraction, scene detection, and video retrieval are presented to validate the effectiveness and efficiency of the proposed algorithms and the performance of the system.     

Fractal analysis of tumor in brain MR images

Abstract. The purpose of this study is to discuss existing fractal-based algorithms and propose novel improvements of these algorithms to identify tumors in brain magnetic-response (MR) images. Considerable research has been pursued on fractal geometry in various aspects of image analysis and pattern recognition. Magnetic-resonance images typically have a degree of noise and randomness associated with the natural random nature of structure. Thus, fractal analysis is appropriate for MR image analysis. For tumor detection, we describe existing fractal-based techniques and propose three modified algorithms using fractal analysis models. For each new method, the brain MR images are divided into a number of pieces. The first method involves thresholding the pixel intensity values; hence, we call the technique piecewise-threshold-box-counting (PTBC) method. For the subsequent methods, the intensity is treated as the third dimension. We implement the improved piecewise-modified-box-counting (PMBC) and piecewise-triangular-prism-surface-area (PTPSA) methods, respectively. With the PTBC method, we find the differences in intensity histogram and fractal dimension between normal and tumor images. Using the PMBC and PTPSA methods, we may detect and locate the tumor in the brain MR images more accurately. Thus, the novel techniques proposed herein offer satisfactory tumor identification. Received: 13 October 2001 / Accepted: 28 May 2002 Correspondence to: K.M. Iftekharuddin     

Dynamic vp-tree indexing for n-nearest neighbor search given pair-wise distances

Abstract. For some multimedia applications, it has been found that domain objects cannot be represented as feature vectors in a multidimensional space. Instead, pair-wise distances between data objects are the only input. To support content-based retrieval, one approach maps each object to a k-dimensional (k-d) point and tries to preserve the distances among the points. Then, existing spatial access index methods such as the R-trees and KD-trees can support fast searching on the resulting k-d points. However, information loss is inevitable with such an approach since the distances between data objects can only be preserved to a certain extent. Here we investigate the use of a distance-based indexing method. In particular, we apply the vantage point tree (vp-tree) method. There are two important problems for the vp-tree method that warrant further investigation, the n-nearest neighbors search and the updating mechanisms. We study an n-nearest neighbors search algorithm for the vp-tree, which is shown by experiments to scale up well with the size of the dataset and the desired number of nearest neighbors, n. Experiments also show that the searching in the vp-tree is more efficient than that for the -tree and the M-tree. Next, we propose solutions for the update problem for the vp-tree, and show by experiments that the algorithms are efficient and effective. Finally, we investigate the problem of selecting vantage-point, propose a few alternative methods, and study their impact on the number of distance computation. Received June 9, 1998 / Accepted January 31, 2000     

Query by video clip

Typical digital video search is based on queries involving a single shot. We generalize this problem by allowing queries that involve a video clip (say, a 10-s video segment). We propose two schemes: (i) retrieval based on key frames follows the traditional approach of identifying shots, computing key frames from a video, and then extracting image features around the key frames. For each key frame in the query, a similarity value (using color, texture, and motion) is obtained with respect to the key frames in the database video. Consecutive key frames in the database video that are highly similar to the query key frames are then used to generate the set of retrieved video clips. (ii) In retrieval using sub-sampled frames, we uniformly sub-sample the query clip as well as the database video. Retrieval is based on matching color and texture features of the sub-sampled frames. Initial experiments on two video databases (basketball video with approximately 16,000 frames and a CNN news video with approximately 20,000 frames) show promising results. Additional experiments using segments from one basketball video as query and a different basketball video as the database show the effectiveness of feature representation and matching schemes.     

Impact of video scheduling on bandwidth allocation for multiplexed MPEG streams

We present efficient schemes for scheduling the delivery of variable-bit-rate MPEG-compressed video with stringent quality-of-service (QoS) requirements. Video scheduling is being used to improve bandwidth allocation at a video server that uses statistical multiplexing to aggregate video streams prior to transporting them over a network. A video stream is modeled using a traffic envelope that provides a deterministic time-varying bound on the bit rate. Because of the periodicity in which frame types in an MPEG stream are typically generated, a simple traffic envelope can be constructed using only five parameters. Using the traffic-envelope model, we show that video sources can be statistically multiplexed with an effective bandwidth that is often less than the source peak rate. Bandwidth gain is achieved without sacrificing the stringency of the requested QoS. The effective bandwidth depends on the arrangement of the multiplexed streams, which is a measure of the lag between the GOP periods of various streams. For homogeneous streams, we give an optimal scheduling scheme for video sources at a video-on-demand server that results in the minimum effective bandwidth. For heterogeneous sources, a sub-optimal scheduling scheme is given, which achieves acceptable bandwidth gain. Numerical examples based on traces of MPEG-coded movies are used to demonstrate the effectiveness of our schemes.     

Block-based manipulations on transform-compressed images and videos

This paper addresses the problem of direct image and video manipulation in compressed domain. The capability to perform such manipulations has become attractive in recent years, as more and more visual information is being captured, stored, and moved in compressed form. Our solution to direct manipulation of compressed images and video is based on a set of block-level transforms, called the inner block transforms (IBTs). Each IBT requires a minimal computation effort in compressed domain to yield a regular geometric transformation on an image block. The paper shows how the IBTs can be used to perform many image and video manipulations. This is done by describing the application of IBTs to image subtitling, shearing, and arbitrary angle rotation. The paper also discusses the application of IBT to M-JPEG and MPEG video. The benefits of the proposed direct compressed domain manipulation approach include faster speed, preservation of image quality, and less computing resources.