Ensuring timeliness and mobility for multimedia computing is a crucial task for wireless communication. Previous algorithms that utilize information channels, such as the information bottleneck method, have shown great performance and efficiency, which guarantees timeliness. However, such methods suit only in handling single variable tasks such as image processing, but are inapplicable to multivariable applications such as video processing. To address this critical shortcoming, we propose a novel tensor information channel which extends the current single-input single-output matrix information channel to a more practical multi-input single-output tensor information channel. In comparison with the classic information channel, our tensor information channel not only performs better in experiments, but also allows for a wider range of practical applications. We further build an innovative tensor-information bottleneck method upon the state-of-the-art information bottleneck method. Experiments on video shot boundary detection are conducted using benchmark data sets to demonstrate the effectiveness of our proposed approach compared with state-of-the-art methods. In specific, our approach yields a 6.2% increase compared with the information channel-based method, and when compared to other state-of-the-art methods, we achieve 0.1%–17.7% performance gains under different experimental configurations.

确保多媒体计算的及时性和移动性是无线通信的关键任务。以前利用信息通道的算法（例如信息瓶颈方法）已显示出出色的性能和效率，从而保证了及时性。但是，这种方法仅适合于处理诸如图像处理之类的单变量任务，而不适用于诸如视频处理之类的多变量应用。为了解决这个严重的缺陷，我们提出了一种新颖的张量信息通道，该通道将当前的单输入单输出矩阵信息通道扩展为更实用的多输入单输出张量信息通道。与传统的信息通道相比，我们的张量信息通道不仅在实验中表现更好，而且还允许更广泛的实际应用。我们将在最新的信息瓶颈方法的基础上进一步构建创新的张量信息瓶颈方法。使用基准数据集进行了视频镜头边界检测的实验，以证明我们提出的方法与最新技术方法相比的有效性。具体而言，与基于信息通道的方法相比，我们的方法可提高6.2％，与其他最新方法相比，在不同的实验配置下，我们可以获得0.1％至17.7％的性能提升。使用基准数据集进行了视频镜头边界检测的实验，以证明我们提出的方法与最新技术方法相比的有效性。具体而言，与基于信息通道的方法相比，我们的方法可提高6.2％，与其他最新方法相比，在不同的实验配置下，我们可以获得0.1％至17.7％的性能提升。使用基准数据集进行了视频镜头边界检测的实验，以证明我们提出的方法与最新技术方法相比的有效性。具体而言，与基于信息通道的方法相比，我们的方法可提高6.2％，与其他最新方法相比，在不同的实验配置下，我们可以获得0.1％至17.7％的性能提升。