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Video classification can be performed by summarizing image contents of individual frames into one class by deep neural networks, e.g., CNN and LSTM. Human interpretation of video content is influenced by the attention mechanism. In other words, video class can be more attentively decided by certain information than others. In this paper, we propose to integrate the attention mechanism into deep networks for video classification.


High-dimensional data structures, known as tensors, are fundamental in many applications, including multispectral imaging and color video processing. Compression of such huge amount of multidimensional data collected over time is of paramount importance, necessitating the process of quantization of measurements into discrete values. Furthermore, noise and issues related to the acquisition and transmission of signals frequently lead to unobserved, lost or corrupted measurements.


In most imaging applications the spatial resolution is a concern of the systems, but increasing the resolution of the sensor increases substantially the implementation cost. One option with lower cost is the use of spatial light modulators, which allows improving the reconstructed image resolution by including a high-resolution codification.


Nowadays, multidimensional data structures, known as tensors, are widely used in many applications like earth observation from remote sensing image sequences. However, the increasing spatial, spectral and temporal resolution of the acquired images, introduces considerable challenges in terms of data storage and transfer, making critical the necessity of an efficient compression system for high dimensional data. In this paper, we propose a tensor-based compression algorithm that retains the structure of the data and achieves a high compression ratio.