Motion adaptive scan rate up-conversion
Multidimensional Systems and Signal Processing
IEEE Transactions on Fuzzy Systems
Adaptive scan rate up-conversion system based on human visual characteristics
IEEE Transactions on Consumer Electronics
Fuzzy Rule-Based Edge-Restoration Algorithm in HDTV Interlaced Sequences
IEEE Transactions on Consumer Electronics
Deinterlacing algorithm using edge direction from analysis of the DCT coefficient distribution
IEEE Transactions on Consumer Electronics
Fuzzy detection of edge-direction for video line doubling
IEEE Transactions on Circuits and Systems for Video Technology
Video de-interlacing by adaptive 4-field global/local motion compensated approach
IEEE Transactions on Circuits and Systems for Video Technology
Iterative second-order derivative-based deinterlacing algorithm
Image Communication
Hi-index | 0.02 |
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.