GPU-Parallel Implementation of the Edge-Directed Adaptive Intra-Field Deinterlacing Method

Abstract

This paper proposes an efficient GPU-based massively parallel implementation of the edge-directed adaptive intra-field deinterlacing method which interpolates the missing pixels based on the deinterlaced covariance estimated from the interlaced covariance according to the geometric duality between the interlaced and the deinterlaced covariance. Although the edge-directed adaptive intra-field deinterlacing method can obtain better visual quality than conventional intra-field deinterlacing methods, the time-consuming computation is usually the bottleneck of this deinterlacing method. In order to tackle the problem, Graphics Processing Units (GPUs), as opposed to traditional CPU architectures, are better candidates to speed up the computation process. The proposed method interpolates more than one missing pixel at a time in order to gain a significant speedup compared to the case of interpolating just one missing pixel at a time. Experimental results show that we obtained a speedup of 94.6 when the I/O transfer time was taken into account, compared to the original single-threaded C CPU code with the -O2 compiling optimization.

Implementación de Algoritmos de Procesamiento Digital de Señales en Hardware Paralelo: Artículo de revisión

“…En total, la aceleración del algoritmo resulto en 143x. En (Wu, Song and Jeon, 2014) se propone una implementación paralela masiva eficiente basada en GPU del método de des-entrelazado en el campo adaptativo dirigido al borde que interpola los píxeles faltantes basados en la covarianza des-entrelazada estimada a partir de la covarianza entrelazada de acuerdo con la dualidad geométrica entre la covarianza entrelazada y des-entrelazada. El método propuesto interpola más de un píxel faltante a la vez con el fin de obtener una aceleración significativa en comparación con el caso de la interpolación de solo un píxel faltante a la vez.…”

Spatio-temporal weight Tai Chi motion feature extraction based on deep network cross-layer feature fusion

“…Adaptive interpolation of spatio-temporal weight can effectively solve the problem of interpolation errors caused by motion estimation errors and inaccurate edge detection. It has the advantage of automatic fusion of edge adaptive field interpolation [21,22]. Firstly, the absolute difference of the pixels before and after the moving image element should be calculated by using the spatio-temporal weight, and the corresponding weight coefficient should be calculated.…”

Rethinking deinterlacing for early interlaced videos

“…To further refine the visual quality, visual saliency is also introduced to improve deinterlacing, for example, spectral residual visual saliency-based deinterlacing [9], [10]. There are also some single field interpolation methods for special applications, such as GPU-parallel implementation of edge-directed interpolation [11] and deinterlacing for 1080p-to-8KUHD [12]. To improve deinterlacing methods for large movements, motionbased methods have been proposed to make use of two fields [13]- [15] or adaptively improve single field methods [16], [17].…”