Temporal Attention Neural Networks for Video Compression

The patent replaces traditional mechanical signal processing systems with neural network-based systems. Specifically, it uses convolutional neural networks (CNNs) and recurrent neural networks (RNNs) to perform motion estimation, optical flow computation, and video reconstruction, substituting conventional algorithmic approaches with learned neural representations that achieve superior compression efficiency.

The patent changes the fundamental parameters of video processing by operating in a latent space rather than direct pixel space. It transforms video frames into latent representations through an encoder network, processes these abstract representations through attention mechanisms and neural networks, then reconstructs the video. This parameter transformation enables more efficient compression by working with compressed latent features rather than raw pixel data.

The patent segments the video processing task into distinct functional modules: an encoder that transforms frames to latent representations, a decoder that reconstructs frames from latent representations, and intermediate processing layers that include attention mechanisms. This segmentation allows each component to specialize in specific operations (motion estimation, spatial feature extraction), improving overall precision while managing computational complexity through modular architecture.

The patent introduces a latent dimension that mediates between input video frames and output reconstructed frames. Instead of processing pixels directly, it transforms them into a lower-dimensional latent space where motion and spatial relationships are represented more compactly. This dimensional transformation enables the attention mechanisms to operate on compressed representations, achieving high measurement precision with reduced computational burden.

The patent performs preliminary action by pre-computing and storing attention weight maps and latent representations during the encoding phase. The attention mechanisms are applied to process only the most relevant spatial and temporal regions identified through these pre-computed attention weights, rather than processing the entire video frame uniformly. This preliminary identification of important regions reduces the effective processing time while maintaining high reconstruction quality.

The patent applies local quality by using spatial and temporal attention mechanisms that dynamically adjust the processing intensity and precision for different regions of the video. Instead of uniform processing, it concentrates computational resources on regions with high motion activity and temporal dependencies, while applying lighter processing to stable regions. This localized quality adjustment improves reconstruction quality where needed without proportionally increasing overall processing time.