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

Video Deinterlacing using Control Grid Interpolation Frameworks

abstract: Video deinterlacing is a key technique in digital video processing, particularly with the widespread usage of LCD and plasma TVs. This thesis proposes a novel spatio-temporal, non-linear video deinterlacing technique that adaptively chooses between the results from one dimensional control grid interpolation (1DCGI), vertical temporal filter (VTF) and temporal line averaging (LA). The proposed method performs better than several popular benchmarking methods in terms of both visual quality and peak signal to noise ratio (PSNR). The algorithm performs better than existing approaches like edge-based line averaging (ELA) and spatio-temporal edge-based median filtering (STELA) on fine moving edges and semi-static regions of videos, which are recognized as particularly challenging deinterlacing cases. The proposed approach also performs better than the state-of-the-art content adaptive vertical temporal filtering (CAVTF) approach. Along with the main approach several spin-off approaches are also proposed each with its own characteristics.Dissertation/ThesisM.S. Electrical Engineering 201

Edge-adaptive spatial video de-interlacing algorithms based on fuzzy logic

Since the human visual system is especially sensitive to image edges, edge-dependent spatial interpolators have been proposed in literature as a means of successfully restoring edges while avoiding the staircase effect of linear spatial algorithms. This paper addresses the application of video de-interlacing, which constitutes an indispensable stage in video format conversion. Classic edge-adaptive de-interlacing algorithms introduce annoying artifacts when the edge directions are evaluated incorrectly. This paper presents two ways of exploiting fuzzy reasoning to reinforce edges without an excessive increase in computational complexity. The performance of the proposed algorithms is analyzed by de-interlacing a wide set of test sequences. The study compares the two proposals both with each other and with other edge-adaptive de-interlacing methods reported in the recent literatur

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

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)

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

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