Network optimisation for remote multimedia imaging applications

Transcription

1 Network optimisation for remote multimedia imaging applications Urban Burnik, Jurij F. Tasič University of Ljubljana Faculty of Electrical Engineering Tržaška 25, SI-1000 Ljubljana, Slovenia phone: fax: Abstract The paper covers an overview of image compression methods that are commonly used in digital telecommunication systems. A proposal is made how adaptive optimisation schemes can be applied in order to provide an optimal level of service quality. The criterion of optimality is set by a currently available network bandwidth, while providing a user with best possible quality of service. The problem, described in the paper, is how to select optimal image compression and transmission parameters, which employ currently available bandwidth and maintains perceptual degradation at a minimum level. The optimisation is constrained by minimum quality of service requirements. 1 Introduction One of the basic requirements of a modern society is to operate on massive information resources. The information processing, transmitting and storing is intensevely moving towards digital techologies. Images represent an important factor in human perception; if speech has been invented to communicate, the word is being sensed and understand mostly by means of visual information. Communication technology allows us to bring reality to other people, rather than by using subjectivised descriptions. Terms like video and television are, in our mind, still related more to entertainment than any other application. We are aware that still image and video information has significant and influential role in all kinds of proffessional applications. Regardless on source and purpose, the amount of digitized visual information is enormous, therefore exhibiting open problems in acquisition, transmission, storage and representation of such data. More still, such amount of data cannot be utilised only by using higher bandwidth connections and more storage capacity. Remote imaging applications require an effective data representation, which can only be achieved by employing optimal data compression algoritms. 1

2 2 Network support for remote imaging applications Current remote imaging applications used in education and telemedicine rely mostly on fixed bandwidth local cable connections, or when required, leased terrestrial and satellite links. The common technology employed is still analog; the level of services in this case is limited to audio and video broadcast in PAL/NTSC quality. Such solutions are costly and the service management is hardly viable, especially when interactive cooperation of multiple participants is required. The currently popular commercial alternative is the use of ISDN network services. Although ISDN is supported by novel packet-based backbone networks, its services basically emulate circuit-switched connection of a fixed n 64 kbps bandwidth. Multipoint connections could be realised over a specialised hardware, to which point-to-point connections of individual participants are to be made. A connection of fixed bandwidth is usualy established before starting a session, and due to severe variations in network traffic, imposed by remote imaging services, a much higher bandwidth is to be allocated (and paid for) to a service that it is statistically required. The very basic approach that deals with the mentioned problem is to establish a basic connection first and to allocate more bandwidth over the available basic ISDN channels only when required. When more services are sharing the same fixed-bandwidth channel, the flexibility and interoperability of applications could further be enhanced by migrating to network utilisation based on statistical multiplexing. The nature of voice, image and data traffic of applications makes this approach a superior one concerning optimality of the network traffic. Such approach is known as a packetswitched network approach. It has been widely accepted for delayed data transfer, however network protocols are being enhanced to allow real time communications. This way, POTS, ISDN and T1 connections are only considered as a media over which a LAN to WAN connectivity is provided. The demanded connection bandwidth may not always be available due to a statistical network behaviour. Due to asynchronous nature of packet-switched network connection data transport faces issues of delay variations and potential packet loss. The imposed delays are highly dependent on type of underlying physical network layer. Real-time data communications put severe constraints on maximum acceptable delay and on retransmittion possibilities. With a restricted bandwidth, delays and data retransmittion, the received data may already be obsolete, causing network congestions only. An efficient remote imaging application should therefore not only handle data requests and maintain good compression rate. To provide an optimal level of service, it has to monitor network behaviour and to adapt the level of services to the momentarily available network resources. 3 Image compression algorithms The problem of digitized image compression is widely addressed by a number of papers and textbooks. The research adresses compression rate improvements, real-time constraints, compressed image quality enhancements as well as specialized topics with specific requirements. Digital image distribution and storage could only become widely accepted, if compatibility will be ensured on an entire path from source to the final user of information. A lot of important work has recently been done on standardisation of compression algorithms for still images and video. 2

3 All compression methods exploit some form of data redundancy and/or irrelevance. We name data redundant, when it is possible to derive a complete data representation by only using partial data elements. Thus, data may be represent in a more compact form without loosing any actual information. Using such methods, a perfect signal representation is provided, with the compression ratio based on the removed redundancy Irrelevance of data results from human perception of the signal. A source coder may remove parts of the signal information in such a way that actually removes some information from the signal, however these effects are not (or barely not) preceived by observers. We name such coders lossy or irreversible. For specific case also some percepual distorsion of the image is allowed in order to obtain very low bit rates. There are several methods to acheive lossy image compression. The most popular methods are based on predictive coding, transform coding and subband coding. Currently, our research covers DCT-based algorithms, although the idea could be extended to other compression methods. Only a brief summary of DCT algorithm will be presented, as it is well known from the literature as JPEG algorithm [2]. Basically, JPEG is a DCT based algorithm, which operates on 8 8 nonoverlapping DCT image blocks. DCT coefficients are then quantised using a perceptually derived quantisation matrix, with the step size of the important low frequency coefficients being finer than those of high frequency coefficients. Each quantised DCT block is then scanned in a zigzag manner (see Figure 1) in order of increasing frequencies to generate a 1-D ordered coefficient stream, whose zero runlenghts and nonzero coefficient values are then losslesly compressed using a 2-D Huffman or arithmetic coding scheme. Figure 1: DCT zigzag scan 4 Network-constraint optimisation An optimal level of quality could only be maintained by using adaptive mechanisms inside the imaging application. The steps inside the basic strategy should follow the algorithm define_service-specific_constraints allocate_required_network_path while service_completed check_network_parameters adjust_qos_to_resources end The optimisation should be performed on-line and should adjust the level and the quality of an undergoing service. Within the frame of available network resources, best possible transfer quality is to be achieved. Standard videoconferencing software features do not satisfy the requirements of technical and medical professionals, whose resolution and quality criteria much differs from general guidelines. The optimisation strategy has to be constrained by parameters which define the lower limits of service quality. In case the network cannot satisfy even minimum requirements, the service is either dropped or replaced by a less demanding substitute (eg. reduced window size at same image resolution). 3

4 Although very convenient in selecting optimisation method, MSE is not reported as a reliable measure of quality in image compression schemes. Experiments show that only 50% correlation with subjective image quality assessments can be achieved [4]. Other quality measures are suggested [5] that better reflect the properties of a human visual system and which are applicable in real-time compression optimisation procedures. These include variations of MSE and Peak Signal to Noise Ratio that incorporate some properties of human visual system [6] and linear predictors [7], [8]. A promising quality assesment metrics called Objective Picture Quality Scale is suggested in [9], with a range of quantitative parameters indicating a quality measure, closely related to subjective quality measurement results. Figure 2: transfer Optimised DCT network image The key elements of an optimised imaging system, applied on a DCT compression scheme, are shown on Figure 2. The figure represents a standard DCT compression algorithm, where quantisation matrix is being changed to ensure optimal image compression. Optimisation criteria are, as seen on Figure 2, defined in terms of compressed image quality and network bandwidth. Optimisation constraints are fixed and are defined by specific service. They include maximum propagation delay and minimum acceptable Quality of Service parameters. Several quality criteria strategies are known from the literature; most of them originates from a simple Mean Squared Error quality metrics, MSE = 1 n ni=1 (X i ˆX i ) 2. Figure 3: Netscape plug-in application 5 Conclusions To evaluate possible optimisation strategies, we developed a Netscape plug-in based application (Figure 3), capable of downloading and retrieving of custom-coded image material. The same application is also capable of performing network delay and temporal bandwidth estimation. The source has been written in C language to allow efficient program execution. A physical testbed is set up on an existing Ethernet IP network located at University of Ljubljana, Slovenia. The network configuration is shown on Figure 4. The presented work is currently undergoing, so the results are only preliminary. Only opti- 4

5 for digital image sequences, Proceedings ICIP-96, Lausanne, Switzerland, September 1996 [6] S. Hangai, K. Suzuki and K. Miyauchi, Advanced WSNR for coded monochrome picture evaluation using fractal dimension,pcs, Sacramento, pp , Figure 4: Testbed network misation based on mean squared error criteria has been tested, and the obtained results are quite promising. The experiments will evolve to better Quality of Service criteria utilisation, as suggested in the paper. References [7] A.A. Webster et al, An objective video quality assessment system based on human perception, SPIE 1913, Human Vision, Visual Processing and Digital Display, pp , [8] W. Xu and G. Hauske, Picture quality evaluation based on error segmentation, SPIE 2308, Visual Communications and ImageProcessing, pp , [9] M. Miyahara, K. Kotani, and V. R. Algazi. Objective Picture Quality Scale (PQS) For Image Coding. Technical report, Center for Image Processing and Integrated Computing, [1] Fuhrt, B., Multimedia Systems: an overview, IEEE Multimedia, 1, [2] Rao, K. R., J. J. Hwang, Techniques and standards for Image, Video and Audio coding, Prentice Hall, NJ, [3] Russell, J. D., Multimedia Networking Performance Requirements, v Y. Viniotis in R. O. Onvural, editors, Asynchronous Transfer Mode Networks, NY:Plenum Press, 1993, pp [4] N. Avadhanam, V. R. Algazi,Prediction and measurement of high quality in stillimage coding, Very High Resolution and Quality Imaging, IS&TSPIE Proceedings Vol. 2663, 1996 [5] Osberger, W., A. J. Maeder, D. McLean, An objective quality assesment technique 5