The low correlation of evaluation indices from the current user perception evaluation model and the neglect of the nonlinear relationship between diversified indices and user experience in different duration videos result in low user perception accuracy of long and short videos. To address these issues, we propose a user experience perception algorithm for long and short videos based on multiple nonlinear regression (LSMNR). First, to improve the efficiency and accuracy of modeling, the algorithm involves preprocessing of video data in edge servers and subdivides the videos based on their duration and popularity. Then, we introduce a new multidimensional quantitative evaluation index that fits the user’s subjective experience and further analyze the influence between multiple evaluation indices (video lag, black screen, etc.) and user quality of experience (QoE) for different video types. Moreover, the characteristics of the data in the multiple evaluation indices are extracted; user subjective evaluation experiments are designed using the video quality expert group (VQEG) standard; and sample and test databases were established. Finally, the optimal model parameters were trained by applying the nonlinear least square method and support vector machine (SVM) to fit and cross-verify the sample data. Our simulation results revealed that the Pearson correlation coefficient of the proposed LSMNR algorithm acquires a value of 0.9810. Compared with algorithms based on multinomial linear regression (MLR), linear SVM, and neural network (NN), the perceptual accuracy of the proposed algorithm is improved by at least 4.0%, and it is applicable to a wider range of video types.

中文翻译：

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基于多重非线性回归的长短视频用户体验感知算法

现有用户感知评价模型评价指标相关性较低，忽视了不同时长视频中多样化指标与用户体验之间的非线性关系，导致长短视频用户感知准确率低。为了解决这些问题，我们提出了一种基于多元非线性回归（LSMNR）的长短视频用户体验感知算法。首先，为了提高建模的效率和准确性，该算法涉及在边缘服务器中对视频数据进行预处理，并根据视频的时长和流行度对视频进行细分。然后，我们引入了一个新的适合用户主观体验的多维量化评价指标，并进一步分析了多个评价指标（视频卡顿、黑屏等）之间的影响。) 和不同视频类型的用户体验质量 (QoE)。此外，提取了多个评价指标中数据的特征；用户主观评价实验采用视频质量专家组（VQEG）标准设计；并建立了样本和测试数据库。最后，通过应用非线性最小二乘法和支持向量机（SVM）对样本数据进行拟合和交叉验证，训练出最优模型参数。我们的模拟结果表明，所提出的 LSMNR 算法的 Pearson 相关系数获得了 0.9810 的值。与基于多项式线性回归（MLR）、线性SVM和神经网络（NN）的算法相比，该算法的感知准确率至少提高了4.0%，适用于更广泛的视频类型。