|Digital Signal Processing||
“Digital economy”, world practice of the development of highly efficient digital systems for TV and radio broadcasting and the problems of its implementation in Russia
The variants of highly efficient digital TV and sound broadcasting and videoconferencing development based on the latest advances in multimedia coding and transmission technology have been analyzed.
Activities on the development of new television broadcasting system variants are carried out through the world. They provide the increase of the quality of transmitted information and viewing perception. These activities include such technologies as ultrahigh definition television (UHDTV), high dynamic range (HDR), high framerate (HFR), immersive multichannel sound accompaniment, etc.
Implementation of the digital terrestrial sound broadcasting in I-III VHF bands is going on gradually. This broadcasting provides the transmission of multimedia information, including both stereo sound with various quality and static and dynamic video signals, other additional digital data.
The most important problem of increasing the efficiency of modern developments is the analysis of the quality of transmitted and reproduced multimedia content. So far, there are no generally unified methods for assessing the quality of service (QoS) and quality of experience (QoE). In this regard, the development of intellectual methods of QoE assessment with the use of machine learning methods to automatically assess the quality of perception on the basis of objective parameters. Sets of the analyzed indicators, ways of their reception and approaches to the analysis are considered.
The results of the new broadcasting systems development allow to state positively about the possibilities of principal change of the information presentation quality. Broadcasting systems don’t realize such functional possibilities up to now. Considering these world achievements, it should be mentioned that they are not deployed in Russia. Insufficient attention is payed to the implementation of digital TV technologies of standard and high definition, deployment of national broadcasting systems that provide sharp increase of radio spectrum usage efficiency, etc.
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Video background reconstruction by non-parametric motion completion and frame-by-frame refinement
Keywords: background reconstruction, motion completion, video processing.
To verify the performance of the proposed background reconstruction algorithm we conduct an experimental comparison with six existing algorithms on seven test video sequences using two quality metrics that have demonstrated high correlation with human perception of background reconstruction quality. According to both metrics, the proposed algorithm demonstrates the best quality on average. Moreover, we provide several examples that illustrate our background reconstruction results and compares them with the results of Huang et al. .
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Over determined systems of equations can be inconsistent because of measurement errors, although often from the physical meaning of the problem it follows that a solution must exist. In this case, you are looking for a pseudo-solution of the system. A pseudo-solution can be sought by the method of least squares. It leads to normal SLAE. An alternative approach is to construct a singular decomposition of the main matrix of the system.
Examples of the solution of over determined systems of equations in real problems of image processing are given. Practical recommendations on finding pseudo-solutions of joint but uncertain systems of equations, as well as incompatible systems of equations in the classical sense, are offered.
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The methods for determining the minimum required number of the recognition process parameters, such as sample volume, number of the spectral informative features, as well as the method for determining the values of wavelengths corresponding to the spectral features, are developed under the given assumptions. The method of determining the minimal required number of the spectral informative features is based on using the given error probability of recognition of two ground objects classes. The method for determining the wavelengths corresponding to the spectral features is based on one-sample t-test. The method of determining the minimum required sample volume is based on two-sample Fisher test. To verify the developed methods, ground experimental hyperspectral measurements were carried out on the test site of Krasnodar.
In time to come, the sample of the minimum required number of spectra with the minimum required number of informative spectral features would move towards a more accurate sampling, which can be used as training data when recognizing classes of ground objects in hyperspectral images obtained from space.
Moreover, the methods presented in this paper will be used for algorithm development of statistical pattern recognition with minimum required recognition parameters values without given assumptions. This algorithm can be used to classify the ground objects with subtle spectral differences, such as the health wheat and wheat leaf rust.
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The article presents the analysis of ways in which multispectral information is displayed and integrated. The private matter is integrating images of different spectrums that are gained in separated ranges, for example, visible and infra-red bands. The classic approach to integrating these images consists of getting a single half-tone image comprising distinctive features of original images. Moreover, all already developed algorithms do not allow as to identify spectrum-dependent objects qualitatively and the resulting integrated image is not always identified with high local contrast of those objects. This is due to the limitation of dynamic range of half-tone images and also due to the diversity of on-site-background situations.
The article offers original algorithms of integrating two digital half-tone images as a color image, this allow us to increase the local contrast of the resulting image containing elements of the original images from one scene, gained in different spectral ranges, and also to simplify identifying spectrum-dependent objects. From the examples, of integrating images using the algorithm it is clear that the color synthesis an effective method for complex decryption of images gained in different ranges of the electromagnetic spectrum. An important feature of the synthesis is that the result of the synthesis with its properties and all other things being equal, is not worse than any spectrozonal image which argues in favour of the use of it in order to reduce the decryption time with the loss of data excluded. The color synthesis is also advantageous in terms of computing costs, for it does not require the image being processed pixel by pixel, but it uses physiological characteristics of the human color vision.
The algorithm can be used in any multispectral servo systems in order to increase their informativeness.
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To obtain experimental data on performance, multithreaded implementation of algorithms in C++ language using OpenCV primitives was performed. The performance of the implemented algorithms was tested on the Intel Xeon 2.4Ghz x 36 CPU configuration, NVIDEA Tesla k20c GPU (706 MHz x 2496).
The conclusion that an algorithm based on SURF descriptors can be effectively paralleled on modern computer technology. Its massive parallel processing implementation on both a multi-core CPU and with the use of video card resources makes it possible to create arrays of coordinates of the same points with a speed that is two orders of magnitude higher than the rate of correlation-extreme search, also realized in parallel. Another issue that determines the feasibility of applying the SURF algorithm is the reliability of the comparison. This requires additional research, which is planned to devote the next work.
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Based on the results from field studies, the effectiveness of each of them was assessed. Revealed that a method of every other frame subtraction had the utmost effectiveness in any kind of meteorological situation, however it required system’s optical axis to be held in stationary position. This causes difficulty when searching and tracking the aerial target. Conclusion was made of feasibility to use a combination of methods, which will provide effective operation of an optico-electronic UAV detector in any kind of meteorological situations.
Proposed a method of combining spatial differentiation distribution of brightness within the line of digital image with convolution operation with expected derivative distribution, determined by the image of a small-sized aerial vehicle. To evaluate the effectiveness of proposed method, the mathematical modeling and field studies were conducted of proposed method of processing of digital images, gotten with the help of thermal imager. This allowed identifying numerical rating of the probability of stable tracking of small-sized aerial target.
Analysis results showed that for a stable tracking of small-sized aerial target, a signal-to-noise ratio has to be not less than 3–4. Proposed method of processing allows achieving, when observing UAV in the background of cumulus clouds, a signs-to-noise ratio equal to 3.3. With other methods, this ratio does not exceed the value of 1.5. This gives the opportunity to conclude of a sufficient effectiveness of proposed combination of methods for processing digital images, gotten with the help of thermal imager when observing small-sized aerial objects.
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The stack of the problem in compact aerial stereoscopic vision systems includes distance estimation task for non-cooperate object. In this work two distance estimation algorithms are proposed. First algorithm is based on 2D template image matching algorithm. It estimates the position of the object on the image, captured by the right camera using the object image that is extracted from the left camera image as template. The distance is calculated by object disparity. This algorithm is precise enough for most application, but its complexity increases with object size growth. Therefore real time work cannot be guaranteed.
The second algorithm is simplified version of the first algorithm. It based of 1D template matching. It uses the 1D cumulative images that produced by image summation by rows and columns. It causes the precision and reliability decreasing, but allows real time implementing in FPGA using pipelining.
The toggling procedure between these two algorithms is based on the object size and algorithm performance indicators analysis. If the object is larger than defined limit and the second algorithm is capable in current condition it is used for the distance estimation. The first algorithm is used otherwise. The performance indicator is based on the difference between the object and the background mean brightness.
The experimental examinations were processed using the database with a number of artificial video sequences with different background. On every sequence object changes its size from 7x7 up to image size (distance changes from 1000 to 100 meters). The proposed algorithm toggling procedure allows estimating distance in real time. The absolute distance estimate error in this case is less than 10 meters.
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The following image processing algorithms are proposed: road marking detection algorithm, vehicle detection and counting algorithm, stopped vehicle detection algorithm. Algorithms are designed to process images obtained from a stationary camera. In our work online processing is performed on the embedded platform of CCTV camera and provides reducing the amount of transmitted data and thus reducing the requirements for communication links and computing server.
The vehicle detection and counting algorithm requires preliminary definition of regions of interest for each road lane. In order to automate this process it is proposed to perform road marking detection for observed section of the road. For this purpose the source image is divided into small blocks which are processed using the integral vector Radon transform (IVRT). The lines found in each block are combined into long lines that are taken as road marking lines. The short lines are connected using the multi-agent approach.
The developed vehicle detection and counting algorithm requires the definition of special regions of interest (the sensors) in the image. Each sensor is divided into two zones (usually the entry zone and exit zone). This allows us to determine the movement direction of a passing car, as well as perform a rough estimation of it speed. Object detection is based on the background estimation in each zone. Also the algorithm takes into account the possibility of shadow condition and excessive lighting.
Stopped vehicle detection algorithm processes the entire image and is based on background subtraction technique. In the proposed algorithm an exponential filter is used to update the back-ground estimation. Our approach is based on current background estimation and a queue of back-ground estimations for various short time intervals. Then we subtract the earliest background estimation from the current background estimation for stopped object detection.
The developed algorithms were implemented and tested on the embedded platform of smart cameras. Analysis of presented results shows the efficiency of algorithms and developed software. We can conclude that presented algorithms work in real time under different observation condi-tions.
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