Recent advances in areas of both the Discrete Wavelet Transforms representing human visual system neural network have resulted in improved video compression, restoration, and filtering techniques. These software techniques are capable of achieving quality performance in video, the computational complexity requires a special design hardware called WaveNet to run a real time live video through radio. The brassboard integrated with computers can potentially provide us many applications including remote sensors, security systems, commercial and home video teleconferencing. This paper describes a low cost board to support video compression (H.263), restoration, and filter system in real time processing. The WaveNet board has been optimized for wavelet-based image and video compression and enhancement techniques.
Video cameras have become a key component for physical security and continue to grow in importance in today's environment. Video cameras often must be installed in remote locations or locations where physical tampering may be a factor. The solution is to transmit the video over wireless communication links. Often, the communication bandwidths are very narrow (typical less than 9.6 kbits). In addition, the image transmission must be made in real time or near time, while still maintaining the integrity or quality of the imagery. This poses a very challenging problem for the transmission of imagery - in particular motion imagery or video. Tridents WaveNet program offers a solution to this problem where the primary objective of this effort is to provide a real time, high quality video compression capability. This paper discusses the WaveNet program with respect to the application of physical security.
There have been numerous approaches for the optimal selection of wavelet basis. Two well known approaches are the 'matching pursuit' and 'entropy based' algorithms. While these approaches have been shown to have good results, they suffer by having large, highly redundant dictionaries in order to represent complex waveforms. In this paper, we present a novel approach for selecting independent wavelet feature basis. In this approach we will leverage the neural net 'super mother' principal along with neural net blind demixing/deconvolution techniques based on the statistical mechanics canonical ensemble for constrained Max-Ent approach with selection of basis may be ideal for independent feature extraction in reducing processing requirement for invariant pattern recognition.
Electro-optical and infrared systems are usually approximated to be Linear Shift Invariant (LSI) systems and are characterized by Modulation Transfer Functions (MTF). Each component in these systems has an MTF that describes the modulation throughput as a function of spatial frequency. Image compression is becoming a component in these systems, but unfortunately, cannot be described by the typical MTF. Image compression output signals can be very different than input signals in not only modulation, but frequency and phase. Therefore, the compression system is neither linear or shift invariant. We proposed an Information Transfer Function (ITF) that can be used in a manner similar to the MTF. This ITF describes the correlation between the input image and the output image as a function of spatial frequency, compression ratio, and image complexity. Like MTF, ITF can be used to determine image compression requirements to maintain system spatial resolution.
There are a large number of EO and IR sensors used on military platforms including ground vehicle, low altitude air vehicle, high altitude air vehicle, and satellite systems. Ground vehicle and low altitude air vehicle (rotary and fixed wing aircraft) sensors typically use the probabilities of discrimination (detection, recognition, and identification) as design requirements and system performance indicators. High altitude air vehicles and satellite sensors have traditionally used the National Imagery Interpretation Rating Systems (NIIRS) performance measures for guidance in design and/or measures of systems performance. Data from the high altitude air vehicle and satellite sensors is now being made available to the warfighter for many applications including surveillance and targeting. National imagery offices are being merged and restructured to more fully support warfighters and connectivities to high altitude air vehicle sensors. It is becoming more apparent that the gap between the NIIRS approach and the probabilities of discrimination approach will have to be addressed. Users, engineers, and analysts need to have a comparative basis for assessing the image quality between the two classes of sensors. This paper describes and compares the two approaches.
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