High performance multimedia database system support for image processing

Analysis of a model for parallel image processing

The aim of digital image processing is to improve the quality of image and subsequently to perform features extraction and classification. It is effectively used in computer vision, medical imaging, meteorology, astronomy, remote sensing and other related field. The main problem is that it is generally time consuming process; Parallel Computing provides an efficient and convenient way to address this issue. Main purpose of this review is to provide the comparative study of the existing contributions of implementing parallel image processing applications with their benefits and limitations. Another important aspect of this study is to provide the brief introduction of parallel computing and currently available parallel architecture, tools and techniques used for implementing parallel image processing. The aim is to discuss the problems encountered to implement parallel computing in various image processing applications. In this research we also tried to describe the role of parallel image processing in the field of medical imaging.

In this paper we present a two-dimensional cellular hypercube architecture for image processing that combines features of the conventional hypercube and cellular logic architectures for 2-D computation cells. A unified theory of parallel binary image processing, binary image algebra (BIA), serves as a software tool for designing parallel image processing algorithms. To match the hardware to the software, we characterize the cellular processors using the same algebraic structure as BIA. The two-dimensional cellular hypercube image processor is a cellular SIMD machine with N2 cells and has a simple overall organization, low cell complexity and fast processing ability. An optical cellular hypercube implementation of BIA is proposed which offers parallel input/output and global interconnection capabilities which are difficult to do in planar VLSI technology.

The problem addressed is how to use transputer networks for image processing. As is well known, SIMD (single instruction/multiple data) or MIMD (multiple instruction/multiple data) architectures are very useful in parallel computing. The authors describe how to use transputers as a SIMD or MIMD machine to implement parallel image processing. Several image-processing algorithms, including Laplacian of Gaussian, edge detection, region labeling, generalized Hough transform, and thinning, have been tested. The authors maintain that transputer-based systems are suitable for real-time image processing. It is also pointed out that, compared with the other parallel machines, transputer systems can provide high performance and more flexibility with relatively low cost. >

This paper provides a tutorial on the motivations, design, and applications of parallel processing applied to video real-time, illustrated by the experience gained in the implementation of the P/sup 3/I machine. Its main purpose is to highlight the motivations for such a development the basic implementation choices, the major difficulties encountered and how they have been solved. Through these studies we found that parallel processing is well-suited to video real-time, when programmable implementations are considered. There are many outcomes of the P/sup 3/I project, ranging from architectural considerations to parallel algorithms optimizations, and programming methodology. We want to emphasize three conclusions. First, programming an architecture composed of different parallel paradigms in a given architecture is tractable, and this heterogeneity is cost effective and efficient in terms of processing performances. Second, concerning the well known debate about how to match parallel architectures and image processing "levels" we conclude that the key is not to discuss Flynn's taxonomy (i.e., data versus tasks parallelism) but to consider how the parallelism grain evolves within a whole application. Third, we confirm that in the field of image processing, the efficiency of parallelism can only be gained if algorithms developers think "parallel"; this result seems to be obvious, but just consider the trends of recent RISC processors, embedding more and more parallelism, and claiming at a compatibility with existing sequential softwares.

In order to speedup image processing in embedded environment, we construct a parallel processing system of images. The parallel processing system includes a parallel hardware, a new defined language for parallel image processing and a set of software tools. From the view of high performance, low power dissipation and the characteristics of image processing, we construct a SIMD coprocessor as an image processing accelerator. Using a RISC host processor manages the whole system. The SIMD processor is scalable. We also define a new language which is extended from standard C. A new data type and a new keyword kernel are added to the language to explicitly describe parallelism. For the new hardware and language of parallel image processing, we also research software tools for this parallel system. The software tools maps programs to code that runs on the new hardware. For example develop a scheduler to transfer stream data between memories and register files. From the analysis we found that the parallel image processing system can not only match image applications' characteristics but also easy to implement by using VLSI technology. With new language and software tools supporting, an embedded real-time parallel image processing system becomes available for programmers.

We have designed and constructed a column parallel vision (CPV) system to realize an intelligent and general purpose image processing system with higher frame rate within 1 millisecond. The system consists of an original designed photo detector array (PDA) with high frame rate, a parallel processing unit with fully parallel processing elements (PEs), and a controller for PDA and PEs. The column parallel architecture enables the PDA 1 millisecond frame rate with 256 analog levels which is required for industrial image processing and measurements. The parallel processing unit has been fabricated by using FPGAs and has 128 X 128 PEs to perform the fully parallel image processing. The PEs have the S<SUP>3</SUP>PE architecture which has SIMD type parallel processing flow, and was constructed only 500 transistors suitable for integration in future. Since the PEs are operated by control signal from the controller, we can achieve desired image processing task to the system by changing the software. We have demonstrated that the system can be worked as a tracking system. The experimental results show the system was realized high speed feedback loop as 1000 frame/s including the tracking operation with noise reduction, matching (self-window algorithm) and moment calculation.

In recent years, the rapid expansion of multimedia applications, partly due to the exponential growth of the Internet, has proliferated over the daily life of computer users (Yang &amp; Hurson, 2006). The integration of wireless communication, pervasive computing, and ubiquitous data processing with multimedia database systems has enabled the connection and fusion of distributed multimedia data sources. In addition, the emerging applications, such as smart classroom, digital library, habitat/environment surveillance, traffic monitoring, and battlefield sensing, have provided increasing motivation for conducting research on multimedia content representation, data delivery and dissemination, data fusion and analysis, and contentbased retrieval. Consequently, research on multimedia technologies is of increasing importance in computer society. In contrast with traditional text-based systems, multimedia applications usually incorporate much more powerful descriptions of human thought—video, audio, and images (Karpouzis, Raouzaiou, Tzouveli, Iaonnou, &amp; Kollias, 2003; Liu, Bao, Yu, &amp; Xu, 2005; Yang &amp; Hurson, 2005). Moreover, the large collections of data in multimedia systems make it possible to resolve more complex data operations such as imprecise query or content-based retrieval. For instance, the image database systems may accept an example picture and return the most similar images of the example (Cox, Miller, &amp; Minka, 2000; Hsu, Chua, &amp; Pung, 2000; Huang, Chang, &amp; Huang, 2003). However, the conveniences of multimedia applications come with challenges to the existing data management schemes: • Efficiency: Multimedia applications generally require more resources; however, the storage space and processing power are limited in many practical systems, for example, mobile devices and wireless networks (Yang &amp; Hurson, 2005). Due to the large data volume and complicated operations of multimedia applications, new methods are needed to facilitate efficient representation, retrieval, and processing of multimedia data while considering the technical constraints. • Semantic Gap: There is a gap between user perception of multimedia entities and physical representation/access mechanism of multimedia data. Users often browse and desire to access multimedia data at the object level (“entities” such as human beings, animals, or buildings). However, the existing multimedia retrieval systems tend to access multimedia data based on their lower-level features (“characteristics” such as color patterns and textures), with little regard to combining these features into data objects. This representation gap often leads to higher processing cost and unexpected retrieval results. The representation of multimedia data according to human’s perspective is one of the focuses in recent research activities; however, few existing systems provide automated identification or classification of objects from general multimedia collections. • Heterogeneity: The collections of multimedia data are often diverse and poorly indexed. In a distributed environment, because of the autonomy and heterogeneity of data sources, multimedia data objects are often represented in heterogeneous formats. The difference in data formats further leads to the difficulty of incorporating multimedia data objects under a unique indexing framework. • Semantic Unawareness: The present research on content-based multimedia retrieval is based on feature vectors—features are extracted from audio/video streams or image pixels, empirically or heuristically, and combined into vectors according to the application criteria. Because of the application-specific multimedia formats, the feature-based paradigm lacks scalability and accuracy.

We discuss possibilities of parallelizing multimedia database systems, especially based on our experiences with our multimedia database system MultiMAP and our parallel database system MIDAS. We describe both initial systems in brief. Our main purpose is to examine when parallelism is of advantage and where sequential processing in multimedia databases is sufficient.

The paper presents a data and task parallel low-level image processing environment for distributed memory systems. Image processing operators are parallelized by data decomposition using algorithmic skeletons. At the application level we use task decomposition, based on the Image Application Task Graph. In this way, an image processing application can be parallelized both by data and task decomposition, and thus better speed-ups can be obtained. We validate our method on the multi-baseline stereo vision application.

In every aspect of today’s professional and private life, information becomes the key to planning, decision making and cooperation. The current widely used databases have been developed for the storing and handling of business data of record and/or table format. Other types of data typically have to be stored in file systems of various complexities. Over the last ten years this deficit of databases has been recognized and new research/development efforts for extended/novel database management systems have been started. Object-oriented, deductive, active, multimedia, and hypertext databases are only some of these systems. In this paper we will argue that multimedia database systems will be needed for the handling of today’s and tomorrow’s information. These data again can be seen under a hypermedia document paradigm. To support these document structures and their manipulation, extensible object-oriented databases are one of the most promising avenues currently explored. We shall illustrate how such an approach can be used at the”data” end to integrate distributed and heterogeneous multimedia information sources and at the”usage” end to provide a broad range of paradigms for domain specific representation and handling mechanisms both in multi-user and cooperating user environments. This flexibility will ultimately be needed to support aspects like ubiquitous or domesticated computing for the information age to come.KeywordsMultimedia DataDatabase SchemaDocument Type DefinitionStandard Generalize Markup LanguageSemantic EnrichmentThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

With the rapid development of microelectronics technology, digital image processing technology has deepened our lives. In the past, image processing mostly used software processing. Due to the large number of processing algorithms and the complexity of the algorithm, it could not meet the real-time requirements. Therefore, this paper proposes a method for parallel processing of ultrasound images based on fax by using a programmable logic device. Image fusion is to extract and synthesize complementary information from different sensors to obtain a more accurate, comprehensive and reliable description of the target or scene. The transmission of images and other images is achieved by the sound of various sensors, which is far from enough. This article presents the use of hardware description language to design image processing and drivers. The core of the print head, scan head and stepper motor can use the control interface to perform selective and parameter variable median filtering on the scanned graphic image through software. Edge sharpening, image field separation, binarization and other hardware processing are compared with a series of processing directly using software, and the optimal processing plan can be made well, and the processed image can be displayed and printed out on the. This design can produce practical results for designs in the fields of biomedicine, sports, and energy production.

Developing embedded parallel image processing applications is usually a very hardware-dependent process, often using the single instruction multiple data (SIMD) paradigm, and requiring deep knowledge of the processors used. Furthermore, the application is tailored to a specific hardware platform, and if the chosen hardware does not meet the requirements, it must be rewritten for a new platform. We have proposed the use of design space exploration [9] to find the most suitable hardware platform for a certain application. This requires a hardware-independent program, and we use algorithmic skeletons [5] to achieve this, while exploiting the data parallelism inherent to low-level image processing. However, since different operations run best on different kinds of processors, we need to exploit task parallelism as well. This paper describes how we exploit task parallelism using an asynchronous remote procedure call (RPC) system, optimized for low-memory and sparsely connected systems such as smart cameras. It uses a futures [16]-like model to present a normal imperative C-interface to the user in which the skeleton calls are implicitly parallelized and pipelined. Simulation provides the task dependency graph and performance numbers for the mapping, which can be done at run time to facilitate data dependent branching. The result is an easy to program, platform independent framework which shields the user from the parallel implementation and mapping of his application, while efficiently utilizing on-chip memory and interconnect bandwidth.

New multimedia applications, such as digital libraries and document warehousing, require next-generation database systems enabling users to efficiently and flexibly develop and execute such networked applications. To this end, we focus on development of a database system which enables flexible and efficient acquisition, storage, access and retrieval, and distribution and presentation of large amounts of heterogeneous media data. We propose a multimedia database system for networked multimedia applications, based on an OODB model extended with agents. We describe an early prototype multimedia database system to verify the proposed approach, which supports multimedia scripts, keyword-based and content-based view retrieval with QoS control, self-organizing map-based clustering and WWW integration.

Structure of 4-core embedded parallel processor is presented and FPGA validation platform that is called FPEP is built.To evaluate performance of multi-core processor platform,three feasible parallel algorithms with OpenMP on SUSAN that is a classic image processing algorithm: direct parallel SUSAN,image block processing and parallel processing of multiple images are proposed,and performance testing of these three parallel algorithms execute on Intel 4-core platform and FPGA validation platform of FPEP.Experimental results demonstrate that speedup ratio of three parallel algorithms on both 4-Core platforms is approximate 3.0,and speedup ratio of parallel processing of multiple images on FPGA validation platform of FPEP is approximate 4.0.