A Fast Gradient Iterative Affine Motion Estimation Algorithm Based on Edge Detection for Versatile Video Coding

Abstract

In the Versatile Video Coding (VVC) standard, affine motion models have been applied to enhance the resolution of complex motion patterns. However, due to the high computational complexity involved in affine motion estimation, real-time video processing applications face significant challenges. This paper focuses on optimizing affine motion estimation algorithms in the VVC environment and proposes a fast gradient iterative algorithm based on edge detection for efficient computation. Firstly, we establish judging conditions during the construction of affine motion candidate lists to streamline the redundant judging process. Secondly, we employ the Canny edge detection method for gradient assessment in the affine motion estimation process, thereby enhancing the iteration speed of affine motion vectors. The experimentalresults show that the encoding time of the affine motion estimation algorithm is about 15–35% lower than the overall encoding time of the anchor algorithm encoder, the average encoding time of the affine motion estimation part of the inter-frame prediction part is reduced by 24.79%, and the peak signal-to-noise ratio (PSNR) is only reduced by 0.04.

Research on Belt Deviation Fault Detection Technology of Belt Conveyors Based on Machine Vision

“…So, it is necessary to use the appropriate filtering means for filtering the image processing, using the traditional Canny edge detection with Gaussian filtering to smooth the image. 2 Calculate the gradient value and direction In calculating the gradient value of an image, the horizontal operator Soble x and the vertical operator Soble y are generally calculated using convolution with the input image to obtain the horizontal and vertical components of the gradient d x and d y [25], as shown in the following equation:…”