Neural Video Encoding Circuitry for Object and Photometric Features

The patent changes the fundamental parameters of video compression by replacing DCT with neural network-based convolutional layers. The first set of convolutional kernels transforms video data into an object representation space, while the second set transforms into a photometric representation space. This parameter change enables adaptation to specific imaging environments and preserves important input statistics that DCT neglects.

The patent substitutes the mechanical/mathematical DCT system with a neural network system. Instead of using fixed mathematical transformation matrices, the invention employs learned convolutional kernels that can adapt to different imaging conditions, objects, and applications through training on specific datasets.

The patent segments the video compression transformation into two distinct functional parts: the first convolutional layer for object representation and the second convolutional layer for photometric representation. This segmentation allows each layer to be optimized for its specific purpose while working together to achieve overall adaptability across different applications.

The neural network-based transformation system achieves multi-functionality by being able to handle different imaging environments, object types, and application requirements through its trained convolutional kernels. The same basic architecture can be adapted to landscape videos, automotive applications, and other specific use cases without requiring fundamentally different transformation methods.