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

An improved algorithm for deinterlacing video streams

The MPEG-4 standard for computerized video incorporates the concept of a video object pLane While in the simplest case this can be the full rectangular frame, the standard supports a hierarchical set of arbitrary shaped planes, one for each content sensitive video object. Herein is proposed a method for extracting arbitrary planes from video that does not already contain video object plane information; Deinterlacing is the process of taking two video fields, each at half the height of the finalized image frame, and combining them into that finalized frame. As the fields are not captured simultaneously, temporal artifacts may result. Herein is proposed a method to use the above mentioned video object planes to calculate the intra-field motion of objects in the video stream and correct for such motion leading to a higher quality deinterlaced output.*; *This dissertation is a compound document (contains both a paper copy and a CD as part of the dissertation)

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

HDTV transmission format conversion and migration path

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1997.Includes bibliographical references (leaves 77-79).by Lon E. Sunshine.Ph.D

Generalized motion and edge adaptive interpolation de-interlacing algorithm

This paper presents a generalized motion and edge adaptive de-interlacing framework, which offers a structured way to develop de-interlacing algorithm. The framework encompasses many typical de-interlacing algorithms, ranging from simple interpolation based algorithms, to more complex edge dependent and motion adaptive algorithms. Based on this framework, we develop a new de-interlacing algorithm which is efficient and artifacts-free. The proposed algorithm was evaluated by five video sequences, namely, "Akiyo", Mother and Daughter", "Silent", "Foreman" and "Stefan". Experimental results confirm that the proposed algorithm performs, both objectively and subjectively, much better than other similar algorithms. These promising results indicate that the proposed framework has good potential for realizing even better de-interlacing algorithms.postprin

Fuzzy logic-based embedded system for video de-interlacing

Video de-interlacing algorithms perform a crucial task in video processing. Despite these algorithms are developed using software implementations, their implementations in hardware are required to achieve real-time operation. This paper describes the development of an embedded system for video de-interlacing. The algorithm for video de-interlacing uses three fuzzy logic-based systems to tackle three relevant features in video sequences: motion, edges, and picture repetition. The proposed strategy implements the algorithm as a hardware IP core on a FPGA-based embedded system. The paper details the proposed architecture and the design methodology to develop it. The resulting embedded system is verified on a FPGA development board and it is able to de-interlace in real-tim

Caveats on the first-generation da Vinci Research Kit: latent technical constraints and essential calibrations

Telesurgical robotic systems provide a well established form of assistance in the operating theater, with evidence of growing uptake in recent years. Until now, the da Vinci surgical system (Intuitive Surgical Inc, Sunnyvale, California) has been the most widely adopted robot of this kind, with more than 6,700 systems in current clinical use worldwide [1]. To accelerate research on robotic-assisted surgery, the retired first-generation da Vinci robots have been redeployed for research use as "da Vinci Research Kits" (dVRKs), which have been distributed to research institutions around the world to support both training and research in the sector. In the past ten years, a great amount of research on the dVRK has been carried out across a vast range of research topics. During this extensive and distributed process, common technical issues have been identified that are buried deep within the dVRK research and development architecture, and were found to be common among dVRK user feedback, regardless of the breadth and disparity of research directions identified. This paper gathers and analyzes the most significant of these, with a focus on the technical constraints of the first-generation dVRK, which both existing and prospective users should be aware of before embarking onto dVRK-related research. The hope is that this review will aid users in identifying and addressing common limitations of the systems promptly, thus helping to accelerate progress in the field.Comment: 15 pages, 7 figure

Fuzzy motion adaptive algorithm and its hardware implementation for video de-interlacing

Interlacing techniques were introduced in the early analog TV transmission systems as an efficient mechanism capable of halving the video bandwidth. Currently, interlacing is also used by some modern digital TV transmission systems, however, there is a problem at the receiver side since the majority of modern display devices require a progressive scanning. De-interlacing algorithms convert an interlaced video signal into a progressive one by performing interpolation. To achieve good de-interlacing results, dynamical and local image features should be considered. The gradual adaptation of the de-interlacing technique as a function of the level of motion detected in each pixel is a powerful method that can be carried out by means of fuzzy inference. The starting point of our study is an algorithm that uses a fuzzy inference system to evaluate motion locally (FMA algorithm). Our approach is based on convolution techniques to process a fuzzy rulebase for motion-adaptive de-interlacing. Different strategies based on bi-dimensional convolution techniques are proposed. In particular, the algorithm called 'single convolution algorithm' introduces significant advantages: a more accurate measurement of the level of motion using a matrix of weights, and a unique fuzzification process after the global estimation, which reduces the computational cost. Different architectures for the hardware implementation of this algorithm are described in VHDL language. The physical realization is carried out on a RC100 Celoxica FPGA development board. © 2010 Elsevier B.V.Comunidad Europea FP7-INFSO-ICT-248858Gobierno de España TIN2005-08943-C02-01 y TEC2008-04920Junta de Andalucía P08-TIC-0367