Interlaced Video Signal Research Articles

Intelligent color image restoration system using cognitive fuzzy concept

Deinterlacing is the procedure of reworking interlaced video signal into a non-interlaced format. The interlaced video signal is generally used in both of analog TV and HDTV with 1080i. The deinterlacing method was designed to solve a legacy issue, therefore the interlaced signal which was requested by analog TV must be trans-formed to be displayed on HDTV. This paper introduces fuzzy concept-based deinterlacing method and shows how it can be executed on various test set. The proposed method includes weight calculation stage and deinterlacing implementation stage. In weight calculation stage, I compute correct weight values on each edge direction, which are calculated using fuzzy concepts. In deinterlacing implementation stage, the computed weight values are used to obtain final deinterlacing pixels by multiplying them with candidate existent pixels. The presented method provides outstanding subjective and objective results in various data set.

Deinterlacing with reduced flickering

Deinterlacing has been used to convert interlaced video signals into progressive video signals. Conventional deinterlacing methods with low levels of complexity can lead to deteriorating video quality, particularly in edge areas. Another problematic artifact is flickering, which occurs more frequently as the video size increases. For example, this flickering artifact is more frequently observed in high-definition television signals. In this paper, we propose a flickering artifact reduction algorithm for deinterlacing, which noticeably increases the overall visual quality.

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

Abstract 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.

A New Intraframe Solution for HDTV to SDTV Down-Conversion

In the last few years, high-definition television (HDTV) technology has been successfully demonstrated, and some products are available for video production. Since HDTV is being introduced gradually, standard definition television (SDTV) will coexist for a long time, therefore, standard converters are necessary to interface equipment and systems of different formats. In this paper, a new solution is proposed for the conversion of interlaced video signals from a high-resolution format down to a low-resolution format, having good performance, low implementation cost, and high design flexibility. Such flexibility can be further exploited for an adaptive implementation.

Preservation of interlaced patterns in encoding video signals using side match vector quantization

Vector quantization (VQ) is a popular class of image encoding techniques which finds valuable applications in developing economical, low bit-rate video conferencing and multimedia products for the consumer's market. A new algorithm known as the side match vector quantization (SMVQ) had been proposed to enhance the visual quality of traditional VQ techniques by reducing the blocking effect based on local edge information. We show that despite the success of SMVQ in encoding still pictures, it does not perform equally well with interlaced video signals which contain motion contents. To overcome this problem, an algorithm known as the hybrid side match vector quantization (HSMVQ) algorithm which enable existing SMVQ to be applied to encode interlaced video signals with improved visual quality is developed and presented. Typical results obtained with HSMVQ, as compared to SMVQ are presented to demonstrate the feasibility of the approach.

Motion detection of an interlaced video signal

This paper describes an advanced algorithm for motion detection from an interlaced video signal, such as PAL or NTSC. A general comparative analysis of motion detectors utilizing one, two, and three field memories is presented. As a potential application, television scan rate conversion, e.g. deinterlacing, is considered. >

Analysis of interlaced video signals and its applications

This paper presents a model of interlaced video. The model uses the assumption that the whole scene, which is captured by a camera, is moving at a constant speed. Within this model, the Fourier transform of the interlaced video signal is analyzed for various modes used in picture coding, such as intrafield, intraframe, interfield (with motion compensation), and interframe (with and without motion compensation) processing. The main spectral components of the signal are identified. The analysis yields new insight into the nature of the components. This insight is useful in the design of video processing systems, which is illustrated by various applications.

The Design Challenges of Full-Motion Video for PC-Compatible Computer Systems

In this article full-motion video applications for PC-compatible computer systems are divided into three subdivisions, according to functionality and application. The issues of the decoding and encoding of composite video signals, and of the keying and overlay of video signals originating from natural sources and signals synthesized by computer graphical means, are discussed as they relate to PC-compatible personal computer systems. The problems presented by the combination of interlaced and noninterlaced video signals, and by the requirements for color-space conversion, are reviewed. A brief discussion of compression schemes for the computerized handling of motion video is offered, followed by a review of design issues relating to image-processing structures and image-capture hardware.

Fixed and Adaptive Predictors for Hybrid Predictive/Transform Coding

Hybrid predictive/transform coding is studied. The usual formulation is to first apply a unitary transform and then code the transform coefficients with independent DPCM coders, i.e., the prediction is performed in the transform domain. This structure is compared to spatial domain prediction, where a difference signal is formed in the spatial domain and then coded by a transform coder. A linear spatial domain predictor which minimizes the mean square prediction error also minimizes the mean square of each transform coefficient. The two structures are equivalent if the transform domain prediction scheme is extended to a more general predictor. Hence, the structure that gives the easiest implementation can be chosen. The spatial domain structure is preferred for motion compensation and for line interlaced video signals. Interframe hybrid coding experiments are performed on interlaced videophone scenes using an adaptive transform coder. Motion compensation gives a rate reduction of 25-35 percent compared to frame difference prediction with the same mean square error. The subjective advantage is even greater, since the "dirty window" effect is not present with motion compensation. It is important to perform the motion estimation with fractional pel accuracy. Field coding with a switched predictor using previous field in moving areas is an interesting alternative to frame coding with frame difference prediction.