A fuzzy motion adaptive algorithm for interlaced-to-progressive conversion

Interlaced-to-progressive algorithms are currently required by video format conversion systems in order to display a progressive scanning used in modern visualization equipments. Deinterlacing algorithms use interpolation techniques to calculate missing pixels in transmitted fields. A motion adaptive algorithm which employs fuzzy logic to adapt the interpolation strategy to the presence of motion in the images is proposed in this paper. The performance of this new approach is evaluated by extensive simulation of different video sequences.Ministerio de Educación y Ciencia TEC2005-04359/MICJunta de Andalucía TIC2006-63

FPGA-based implementation of a fuzzy motion adaptive de-interlacing algorithm

This paper surveys the hardware implementation of a de-interlacing algorithm on Field-Programmable Technology for real-time processing. The algorithm presented evaluates the level of motion at each pixel, and determines the interpolation between a spatial and a temporal method according to the presence of motion. To achieve it the algorithm employs an hierarchical structure with three simple fuzzy systems. The first one performs a set of fuzzy rules to apply reasoning in order to detect motion; the second one selects the most convenient direction to implement an edge-dependent line average method; and the third one is used to choose the most adequate temporal method. The hardware implementation of this algorithm combines pipeline architecture with a parallel processing of fuzzy rules to accelerate the computation. As result an efficient implementation is developed in terms of computational time and hardware cos

Adaptive deinterlacing of video sequences using motion data

In this work an efficient motion adaptive deinterlacing method with considerable improvement in picture quality is proposed. A temporal deinterlacing method has a high performance in static images while a spatial method has a better performance in dynamic parts. In the proposed deinterlacing method, a motion adaptive interpolator combines the results of a spatial method and a temporal method based on motion activity level of video sequence. A high performance and low complexity algorithm for motion detection is introduced. This algorithm uses five consecutive interlaced video fields for motion detection. It is able to capture a wide range of motions from slow to fast. The algorithm benefits from a hierarchal structure. It starts with detecting motion in large partitions of a given field. Depending on the detected motion activity level for that partition, the motion detection algorithm might recursively be applied to sub-blocks of the original partition. Two different low pass filters are used during the motion detection to increase the algorithm accuracy. The result of motion detection is then used in the proposed motion adaptive interpolator. The performance of the proposed deinterlacing algorithm is compared to previous methods in the literature. Experimenting with several standard video sequences, the method proposed in this work shows excellent results for motion detection and deinterlacing performance

Evaluation of the color image and video processing chain and visual quality management for consumer systems

With the advent of novel digital display technologies, color processing is increasingly becoming a key aspect in consumer video applications. Today’s state-of-the-art displays require sophisticated color and image reproduction techniques in order to achieve larger screen size, higher luminance and higher resolution than ever before. However, from color science perspective, there are clearly opportunities for improvement in the color reproduction capabilities of various emerging and conventional display technologies. This research seeks to identify potential areas for improvement in color processing in a video processing chain. As part of this research, various processes involved in a typical video processing chain in consumer video applications were reviewed. Several published color and contrast enhancement algorithms were evaluated, and a novel algorithm was developed to enhance color and contrast in images and videos in an effective and coordinated manner. Further, a psychophysical technique was developed and implemented for performing visual evaluation of color image and consumer video quality. Based on the performance analysis and visual experiments involving various algorithms, guidelines were proposed for the development of an effective color and contrast enhancement method for images and video applications. It is hoped that the knowledge gained from this research will help build a better understanding of color processing and color quality management methods in consumer video

Application of deinterlacing for the enhancement of surveillance video

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2001.Includes bibliographical references (p. 91-93).As the cost of video technology has fallen, surveillance cameras have become an integral part of a vast number of security systems. However, even with the introduction of progressive video displays, the majority of these systems still use interlaced scanning so that they may be connected to standard television monitors. When law enforcement officials analyze surveillance video, they are often interested in carefully examining a few frames of interest. However, it is impossible to perform frame-by-frame analysis of interlaced surveillance video without performing interlaced to progressive conversion, also known as deinterlacing. In most surveillance systems, very basic techniques are used for deinterlacing, resulting in a number of severe visual artifacts and greatly limiting the intelligibility of surveillance video. This thesis investigates fourteen deinterlacing algorithms to determine methods that will improve the quality and intelligibility of video sequences acquired by surveillance systems. The advantages and disadvantages of each algorithm are discussed followed by both qualitative and quantitative comparisons. Motion adaptive deinterlacing methods are shown to have the most potential for surveillance video, demonstrating the highest performance both visually and in terms of peak signal-to-noise ratio.by Brian A. HEng.S.M

Iris Recognition: Robust Processing, Synthesis, Performance Evaluation and Applications

The popularity of iris biometric has grown considerably over the past few years. It has resulted in the development of a large number of new iris processing and encoding algorithms. In this dissertation, we will discuss the following aspects of the iris recognition problem: iris image acquisition, iris quality, iris segmentation, iris encoding, performance enhancement and two novel applications.;The specific claimed novelties of this dissertation include: (1) a method to generate a large scale realistic database of iris images; (2) a crosspectral iris matching method for comparison of images in color range against images in Near-Infrared (NIR) range; (3) a method to evaluate iris image and video quality; (4) a robust quality-based iris segmentation method; (5) several approaches to enhance recognition performance and security of traditional iris encoding techniques; (6) a method to increase iris capture volume for acquisition of iris on the move from a distance and (7) a method to improve performance of biometric systems due to available soft data in the form of links and connections in a relevant social network

Intelligent Side Information Generation in Distributed Video Coding

Distributed video coding (DVC) reverses the traditional coding paradigm of complex encoders allied with basic decoding to one where the computational cost is largely incurred by the decoder. This is attractive as the proven theoretical work of Wyner-Ziv (WZ) and Slepian-Wolf (SW) shows that the performance by such a system should be exactly the same as a conventional coder. Despite the solid theoretical foundations, current DVC qualitative and quantitative performance falls short of existing conventional coders and there remain crucial limitations. A key constraint governing DVC performance is the quality of side information (SI), a coarse representation of original video frames which are not available at the decoder. Techniques to generate SI have usually been based on linear motion compensated temporal interpolation (LMCTI), though these do not always produce satisfactory SI quality, especially in sequences exhibiting non-linear motion. This thesis presents an intelligent higher order piecewise trajectory temporal interpolation (HOPTTI) framework for SI generation with original contributions that afford better SI quality in comparison to existing LMCTI-based approaches. The major elements in this framework are: (i) a cubic trajectory interpolation algorithm model that significantly improves the accuracy of motion vector estimations; (ii) an adaptive overlapped block motion compensation (AOBMC) model which reduces both blocking and overlapping artefacts in the SI emanating from the block matching algorithm; (iii) the development of an empirical mode switching algorithm; and (iv) an intelligent switching mechanism to construct SI by automatically selecting the best macroblock from the intermediate SI generated by HOPTTI and AOBMC algorithms. Rigorous analysis and evaluation confirms that significant quantitative and perceptual improvements in SI quality are achieved with the new framework