Year-end Sale % OFF 
Abstract
To improve the video quality, aiming at the problems of low peak signal‐to‐noise ratio, poor visual effect, and low bit rate of traditional methods, this paper proposes a fast compensation algorithm for the interframe motion of multimedia video based on Manhattan distance. The absolute median difference based on wavelet transform is used to estimate the multimedia video noise. According to the Gaussian noise variance estimation result, the active noise mixing forensics algorithm is used to preprocess the original video for noise mixing, and the fuzzy C‐means clustering method is used to smoothly process the noisy multimedia video and obtain significant information from the multimedia video. The block‐based motion idea is to divide each frame of the video sequence into nonoverlapping macroblocks, find the best position of the block corresponding to the current frame in the reference frame according to the specific search range and specific rules, and obtain the relative Manhattan distance between the current frame and the background of multimedia video using the Manhattan distance calculation formula. Then, the motion between the multimedia video frames is compensated. The experimental results show that the algorithm in this paper has a high peak signal‐to‐noise ratio and a high bit rate, which effectively improves the visual effect of the video.
Highlights
In recent years, with the rapid development of multimedia and network technology, video, image, computer vision, multimedia database, and computer network technology are increasingly integrated, covering all aspects of the national economy and social life
To improve the peak signal-to-noise ratio, the visual effect, and the bit rate of multimedia video images, a fast interframe motion compensation algorithm based on the Manhattan distance is proposed in this paper
To verify the effectiveness of the fast interframe motion compensation algorithm of the multimedia video based on the Manhattan distance proposed in this paper, the compensation algorithm based on depth learning and the compensation algorithm based on depth convolution neural network are used as comparison methods, and the application effects of different methods are judged according to the experimental results
Summary
With the rapid development of multimedia and network technology, video, image, computer vision, multimedia database, and computer network technology are increasingly integrated, covering all aspects of the national economy and social life. The commonly used interframe motion compensation method of the video image is to intensively match the input image pairs based on the optical flow field estimation algorithm and interpolate the input image pixel by pixel using the obtained dense matching information to synthesize the intermediate frame image. Reference [5] proposes a deep convolution neural network algorithm to realize image interframe compensation. E experimental results show that the image frame compensation method based on a deep convolution neural network can effectively solve the problem of image loss, the bit rate is low and the application effect is poor. To improve the peak signal-to-noise ratio, the visual effect, and the bit rate of multimedia video images, a fast interframe motion compensation algorithm based on the Manhattan distance is proposed in this paper.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
