Multimedia asset management for post-production
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1 Multimedia asset management for post-production Ciaran Wills Department of Computing Science University of Glasgow, Glasgow G12 8QQ, UK Abstract This paper describes the ongoing development of tools for assisting the management of large volumes of visual data in film and television postproduction. By integrating recently developed content-based image retrieval techniques into an existing asset tracking framework, we can facilitate reuse of archived material by making the contents of the archive searchable on the basis of visual content. We can also produce summaries of video sequences which reduce them to the essential objects and action, allowing low bandwidth transmission and the generation of easily understood 2-D comic strip summaries. 1 Introduction Video and image database technology has become an active academic and commercial research area during the 1990s the cost of computing power and storage is now low enough that large libraries of multimedia data can be stored and processed online using inexpensive hardware. Post-production is an activity which stands to benefit enormously from the application of multimedia database technology. Briefly, postproduction is the processing of raw video or film into the final product for broadcast or distribution. Specialist post-production facilities handle complex effects work for film and television, particularly for adverts, which use extensively manipulated footage. A typical post-production house handles many jobs for many clients, involving a large amount of video data, much of which is archived after the job is complete. An asset management system can assist the tracking of jobs in progress throughout the facility, providing quick access to all the visual material involved. Combined with a web interface, clients can view the current status of their job from off-site. Access to the archive can also be improved, providing fast identification and location of archived material, making the reuse of archived material a realistic prospect. This translates into direct economic benefits for the facility. Time in a post-production suite is expensive (hundreds of pounds per hour) and manually searching for material is a lengthy process, as tapes must be found, loaded and viewed. Shooting new footage is also an expensive undertaking, so reusing archived material can produce substantial time and cost savings. This work is supported by two London based post-production companies Smoke & Mirrors [1] and Unique-ID [2] and close cooperation with them means techniques can be tailored to the specialised needs of a post-production environment. As the system is developed, it will be integrated into their production workflow, giving early feedback of the system s effectiveness. 1.1 Background Content-based image retrieval (CBIR) is an active research area, there are several commercial offerings from database vendors who have extended their systems to handle images and video, and many academic papers have been published. For an overview of recent work in the area, see [3]. Almost all systems work by computing a set of features which empirically describe the perceptual properties of an image or sequence such as colour, shape or texture. These features are then concatenated into a feature vector which is stored in a
2 Facility network Desktop PC Fileserver Daemon Desktop PC Video Server Database Server Cakes servers Private network Digitiser VCR Digitiser VCR Figure 1: Architecture of the cakes system. Video out Video out Every tape has a magnetic transponder with a unique identifier which is scanned by a reader on the video deck; every frame on the tape can then be uniquely identified by the combination of this identifier and the timecode. The reader is positioned so that it is not possible to insert a tape without it being scanned, so no manual effort is required to keep the system up to date. Figure 1 shows how the cakes system fits into the facility s existing network. A web interface lets users access the metadatabase, and video clips can be viewed on the user s desktop through streaming video. By applying content based image retrieval techniques to the online proxy images, we can extend the abilities of the cakes system. database using high dimensional indexing or dimensionality reduction. A similarity measure is defined over the feature space, giving an empirical measure of how close one image is to another. Two models for accessing the database are typically used; browsing, where the user can view many items which are close in the feature space (and hence similar), or query-by-example, where the user provides a sample image or sequence, and the system computes its feature vector and returns similar items from the database. 1.2 The cakes system Cakes is a media asset tracking system for postproduction facilities, produced by Unique-ID. Using a system which uniquely identifies material, it keeps a metadatabase with information about each asset, such as location, the job and client it is associated with, whether it is derived from another asset, and who it has been distributed to. Cakes automatically generates low resolution proxies of all video that passes through the facility. As material is played out or recorded to tape, a digitising station attached to the video deck snoops on the video signal and generates a quarter resolution snapshot of each frame; these are stored on a video server in JPEG format. Material that is stored online on the editing machines is identified by a daemon which periodically scans the filesystems, generating proxies for new or modified material. 2 Segmentation To provide effective indexing of video material, we need to build a content based representation which reflects the semantic structure of each sequence. To this purpose we break all video down into atomic units in time (shots) and space (objects). 2.1 Temporal segmentation An essential preprocessing step is the partitioning of the unstructured video stream into separate shots. We have developed a robust algorithm which operates directly on the JPEG compressed video frames. Many temporal segmentation algorithms have been proposed, operating in both the compressed and uncompressed domains. Nearly all the published algorithms are tested using sample video from television programs and movies; however we found that most were not robust enough to handle the diversity of material found in a post-production environment. This ranges from finished adverts which are typified by short shots and complex transitions and effects, and raw footage which contains a lot of noise, exposure and focus adjustments, and unintentionally filmed shots (i.e. the camera pointing at the floor). Table 1 shows the shot length statistics for some typical advertising material; the first item is an agency s promotional showreel, the second an advert for lollipops, and the last an advert for nap-
3 pies. Shot lengths are typically under a second, and can even be as short as one or two frames. Figure 2 shows a common artefact the white flash. This can be caused either by over exposure of the film, or added deliberately as an effect. It causes problems for many segmentation algorithms as it causes changes in all the image statistics including the colour histogram and contrast, which many algorithms use to decide where shot boundaries are. However a white flash doesn t necessarily indicate a transition (although it is a significant event in the sequence and should be flagged) Our approach Our segmentation algorithm operates on a stream of JPEG compressed frames, examining them at the macroblock level and deciding if each block is in transition, based on local information in the spatial and time dimensions. When a large enough percentage of the macroblocks in the image are found to be undergoing the same transition, we decide that the stream should be segmented at that point. The DCT coefficients of each macroblock provide information about the mean intensity and colour, and the level of detail. We can also cross-correlate blocks in the frequency domain to find if their content is similar. These statistics are used to label each block as either: changed There is a large change in intensity, colour or detail compared to blocks before and after. fading out There is a gradual reduction in detail, and slow change in colour and intensity. fading in There is a gradual increase in detail, and slow change in colour and intensity. unknown A conclusive decision cannot be made. A series of fade out frames followed by fade in frames without a constant coloured frame between indicates a gradual transition between two shots; by examining the content of the frames before and after the transition, we can identify if a white flash has occurred. The algorithm does not, however, detect spatial transitions such as wipes. The algorithm also strips out blank and noise frames, and can run at about 90 frames per second on a 400MHz Pentium II, although file system overhead usually causes it to run slower (as we store each frame as a separate file). 2.2 Spatial segmentation Within a single shot, we wish to identify the significant objects. It is not possible to build a complete semantic understanding of the scene in the computer, so we must make some assumptions to map the low level representation of the shot to a higher level object based understanding. These assumptions are: (i) that object boundaries are aligned with colour or intensity edges, and (ii) that a moving coherent region is a significant object. Working on these assumptions we develop a spatial segmentation algorithm which combines colour and motion information to identify the coherent moving objects in the shot. The algorithm begins with a colour segmentation of a single frame, generated using the spanning tree technique of [7]. This produces a binary tree representation of the frame; each node represents a connected region with the root representing the whole frame. The children of each node represent a split along the colour boundaries in that region Motion estimation Motion parameters are computed for a region R using a multiresolution robust gradient based technique [8]. Motion over a region between frames I 1 and I 2 is modelled with a quasi-quadratic model which defines the flow at each pixel (x, y) as a function of eight parameters: u x = a 1 + a 2 x + a 3 y + a 7 x 2 + a 8 xy u y = a 4 + a 5 x + a 6 y + a 7 xy + a 8 y 2 A Gauss-Newton step is iterated at each resolution to find the parameters which minimize the objective function i R ρ(r i, σ) where r i is the Displaced Frame Difference for pixel i r i = I 2 (x i, y i ) I 1 (x i u x, y i u y ) and ρ is the Lorentzian M-estimator
4 mean median σ minimum total shots total time Showreel 0.65s 0.62s s s Advert s 0.78s s s Advert s 1.28s s s Table 1: Shot length statistics of three video clips (σ = standard deviation). Figure 2: White flash. ( ρ(r, σ) = log r 2 σ 2 The scale parameter σ is calculated for each iteration from the median of the residuals σ = median i r i Each Gauss-Newton step computes the gradient in the parameter space and performs a line minimization of the objective function in that direction. The iteration terminates when the change in the parameters is below a given threshold Object detection We are currently investigating different techniques for extracting the moving objects from the sequence, and describe a couple of possible approaches here. The moving regions can be tracked throughout the sequence, and we can detect and discard any spurious objects produced by erroneous motion estimation. Given the spatial segmentation and a motion model for each region we can take either a top-down or a bottom-up approach to finding the regions of coherent motion. Top-down The top-down approach begins by estimating the motion for the whole frame. The frame is then warped by the estimated motion parameters and compared to the actual motion in the ) next frame. If there is a significant error between the two the operation is repeated on the child nodes of the root node. This continues down the tree until the error for each motion is below a threshold where we assume that the region is part of a single object. However an object may have been split into two or more separate regions of coherent motion as the structure of the spatial segmentation tree is unlikely to reflect the semantic structure of the scene. Neighbouring coherent regions are then compared to see if they have similar motion models, and merged as in the bottom-up approach. Bottom-up We begin with a fine-grain spatial segmentation of the scene, and assume that each small region is part of a single object (although the regions must be large enough to reliably compute the motion parameters). Regions are compared with their neighbours and merged if their motion models are similar. 3 Video summarisation The segmentation process extracts the essential information from a sequence, preserving the semantic content of the sequence while drastically reducing the amount of data. This presents several possibilities for recreating the data. Descriptions of the objects in a scene (colour, texture, shape, movement) can be transmitted using much less bandwidth than the original video, even at reduced resolution. Thus a much reduced
5 Original image Small regions (>50 pixels) Static background Regions with coherent motion Whole image Background Foreground Smaller regions Tree structure guage allowing the reader to quickly grasp what is happening within each frame of the story [5]. By exploiting the conventions of comic books we can render what is basically a comic strip version of a video sequence. Such conventions include movement lines to indicate action, and emphasising foreground objects with stronger colours and outlines. We can also incorporate the conventional annotations used by storyboard artists to indicate video specific features such as camera movements. Figure 3: Spatial segmentation using colour and motion. version, yet still containing the essence of the sequence, could be transmitted over a low bandwidth communication channel, such as a modem or mobile telephone. 3.1 Storyboards Storyboards are a very common tool in video production; the outline of the sequence is sketched out in a series of drawings, showing the desired framing, and the principal objects and actions for each shot [6]. The storyboard is then used as a guide during shooting or animation. The basic information conveyed by the storyboard mirrors what we have extracted in the segmentation process. Thus we can reverse the process, constructing a static 2D representation of a video sequence which shows the outline of the action. This is a valuable summary, which can be viewed and understood quicker than watching the whole sequence, and is more easily distributed than a video tape or streaming video as it requires no equipment to view. Previous video summarisation systems have presented a series of keyframes, one representing each shot; keyframes are either chosen arbitrarily or by selecting the keyframe that is deemed most representative of the shot, or by mosaicing several frames together into a single image. Some systems also show the relationships between shots within the sequence [10], and the Video Manga system [9] emphasises some shots over others by using different sizes of keyframes. Storyboards have strong parallels with comic book art, which has developed a stylistic visual lan- 4 Retrieval The content information extracted during the segmentation process can be used to index and search material in the video archive. In interviews with producers, they have said that archive material is rarely reused mainly because searching for suitable material is a lengthy manual process. They would like to reuse material more often, as acquiring new footage is expensive, and can delay production schedules. There have been exceptions, where an advert has been produced using mostly recycled material, but these rely on the producers own familiarity with material they have worked on before which is now in the archive. Indexing the information gathered during the segmentation phase, combined with the online proxies, allows quick browsing and searching of archived material from a desktop computer. A producer can view material and show it to clients on a PC in the editing suite, and only after deciding to use the material does the tape have to be retrieved from the archive. 4.1 Search features and interface Potential users of the searching and browsing interface have been interviewed to ascertain what properties are desirable in the system; which features of a sequence should be used as search criteria, and what form of interface should the system have. The most important search criteria cited was movement; how the camera moves, and how the objects within the frame move. The framing of the shots was also important, and the shape of the moving objects was also a desirable feature to specify. Colour was not deemed as relevant because it can be easily changed in the editing process; however
6 it is an important recognition cue, and is useful for locating items the user has seen before. The compositors liked the idea of a sketch based interface to the system. Most of their work is carried out using a graphics tablet and they can quickly and accurately draw what they are looking for. The interface should understand the storyboarding conventions for specifying object and camera movement. 5 Conclusions This paper has briefly described how we are applying content-based image retrieval techniques to a specific application area; television and film postproduction. CBIR techniques have been seen as a solution looking for a problem [4]; post-production is an activity which will benefit greatly from application of research in this area. Initial response from the post-production industry has been very encouraging, and we anticipate further interesting results as this project progresses. Video Technology, 9(8): , December [8] Harpreet S. Sawhney and Serge Ayer. compact representations of video through dominant and multiple motion estimation. IEEE Transactions on PAMI, 18(8): , August [9] Shingo Uchihashi, Jonathan Foote, Andreas Girgensohn, and John Boreczky. Video manga: generating semantically meaningful video summaries. In Multimedia 99, pages ACM, [10] Boon-Lock Yeo and Minerva M. Yeung. Classification, simplification and dynamic visualisation of scene transition graphs for video browsing. In Ishwar K. Sethi and Ramesh C. Jain, editors, Storage and Retrieval for Image and Video Databases VI, volume 3312 of Proceedings of SPIE, pages SPIE, References [1] Smoke & mirrors. [2] Unique-id software ltd. [3] Alberto Del Bimbo. Visual information retrieval. Morgan Kaufmann, [4] E. J. Delp. Video and image databases: who cares? In Minerva M. Yeung, Boon-Lock Yeo, and Charles A. Bouman, editors, Storage and Retrieval for Image and Video Databases VII, volume 3656 of Proceedings of SPIE, pages SPIE, [5] Will Eisner. Graphic storytelling. Poorhouse Press, [6] John Hart. The art of the storyboard. Focal Press, [7] P. Salembier and F. Marqués. Region-based representations of image and video: segmentation tools for multimedia services. IEEE Transactions on Circuits and Systems for
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