Application of Improved Image Processing Technology in New Media Short Video Quality Improvement in Film and Television Postproduction

Yi Wang
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Abstract

With the development of film and television industry and the rise of new media short video, film and television postproduction process has higher and higher requirements for image quality. In film and television postproduction, optimizing the image quality can enhance the resolution and make the image more vivid and detailed. High-quality image can fully embody the value of film and television, as well as promote the development of new media short videos. This paper optimizes image quality by improving image processing technology, thus improving the quality and value of film and television and new media short videos. In this paper, a convolutional neural network combined with a nonlinear activation function is used to establish an improved image processing technology model to efficiently extract image features. This technology can enhance the ability to extract image features, improve the accuracy of image feature extraction, and then improve the image resolution and details, thus improving the image quality in the process of film and television postproduction. The results show that the average value of PSNR is 30.29. The average value of PSNR of the proposed algorithm is higher than that of other algorithms, indicating that the error between the image processed by the proposed algorithm and the original image is small. The average SSIM of the algorithm in this paper is 0.903, which is closer to 1. Compared with other algorithms, the structure processed by the algorithm in this paper is more similar to the original structure, resulting in a better graph. The algorithm in this paper has the best performance on both the peak signal-to-noise ratio (PSNR) and the structural similarity (SSIM). The improved image processing technology proposed in this paper can effectively improve the accuracy of image feature extraction, making film and television or new media short video images of higher quality.
改进图像处理技术在新媒体影视后期短视频质量提升中的应用
随着影视产业的发展和新媒体短视频的兴起,影视后期制作过程对图像质量的要求越来越高。在影视后期制作中,优化图像质量可以提高分辨率,使图像更加生动细致。高质量的影像能充分体现影视的价值,也能促进新媒体短视频的发展。本文通过改进图像处理技术来优化图像质量,从而提高影视和新媒体短视频的质量和价值。本文采用卷积神经网络结合非线性激活函数建立改进的图像处理技术模型,有效提取图像特征。该技术可以增强提取图像特征的能力,提高图像特征提取的准确性,进而提高图像的分辨率和细节,从而提高影视后期制作过程中的图像质量。结果表明,PSNR均值为30.29。本文算法的PSNR平均值高于其他算法,说明本文算法处理后的图像与原始图像的误差较小。本文算法的平均SSIM为0.903,更接近于1。与其他算法相比,本文算法处理的结构更接近于原始结构,从而得到更好的图。该算法在峰值信噪比(PSNR)和结构相似度(SSIM)两方面均具有最佳性能。本文提出的改进图像处理技术可以有效提高图像特征提取的精度,使影视或新媒体短视频图像质量更高。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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