Neural Network Video Encoder Exponential Family Prior Regularization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current video coding techniques face challenges in achieving better coding efficiency and rate control to compress high-quality video data, which demands large amounts of data, placing a burden on communication networks and devices.
Innovation Solution
The use of neural networks with exponential-family priors for data quantization, specifically applying a univariate exponential-family prior to the outputs of neural network layers to generate evaluations and constraints, which are then used to adjust the loss values and train the neural network-based video encoder.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If neural networks are used for video encoding to improve coding efficiency, then video compression performance is improved, but training time and computational complexity increase
Solution Approach 1:
The patent applies exponential family priors to change the parameter distribution assumptions in the neural network layers, enabling more efficient training by transforming the optimization problem into a form that can be solved with standard statistical methods rather than requiring complex iterative optimization
Solution Approach 2:
The patent replaces traditional mechanical gradient descent optimization with a probabilistic approach using exponential family distributions, substituting the iterative numerical optimization process with a more efficient statistical framework that leverages closed-form solutions
2Quantity of substance
If quantization steps are applied in neural network layers to reduce data precision, then data transmission burden is reduced, but modeling accuracy deteriorates
Solution Approach 1:
The patent changes the parameter distribution assumptions by introducing exponential family priors that accommodate quantized data, allowing the model to maintain accuracy despite reduced precision through proper statistical characterization of the quantization process
Solution Approach 2:
The patent introduces exponential family prior distributions as an intermediary between the quantized data and the neural network parameters, serving as a bridge that preserves information about the quantization process and enables accurate reconstruction without requiring full precision
3Stability of the object's composition
If normalization functions are applied to neural network outputs to stabilize training, then training convergence is improved, but computational complexity increases
Solution Approach 1:
The patent makes the normalization process self-service by using the exponential family prior to automatically characterize the output distribution, eliminating the need for separate manual normalization operations and allowing the model to self-regulate through the prior distribution
Data Source
AI summary
Systems and techniques are described for processing video data. For instance, a process can include processing a frame of video data using a first layer of a neural network-based video encoder, the neural network-based video encoder performing at least one quantization step. The process can further include applying an exponential-family prior to an output of the first layer of the neural network-based video encoder to generate a first layer output evaluation, generating a total loss value for the neural network-based video encoder based on a sum of a loss value for the neural network-based video encoder and the first layer output evaluation, and training the neural network-based video encoder based on the total loss value.