Entropy-Constrained Neural Video Representation
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Solution Overview
Problem
Current neural video representation methods are outperformed by traditional techniques in lossy video compression due to inefficient handling of temporal and spatial input coordinates and disjoint minimization of rate and distortion, often relying on heuristic post-training quantization or weight pruning.
Innovation Solution
An entropy-constrained neural video representation approach using a novel convolutional-based neural network architecture that models the entropy of the representation and optimizes rate-distortion jointly during training, enabling faster encoding and decoding with a unified solution for video representation and compression.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional video compression techniques are used, then compression efficiency is maintained, but neural network-based approaches fail to achieve comparable performance
Solution Approach 1:
The patent merges the rate and distortion optimization processes into a unified joint optimization framework. Instead of separately minimizing rate (compression ratio) and distortion (quality loss), the system optimizes both simultaneously through a combined loss function that balances compression efficiency and visual quality, resolving the performance gap between neural and traditional methods
Solution Approach 2:
The patent changes the optimization parameters by introducing entropy constraints and joint rate-distortion loss functions. This transforms the separate optimization approach into a parameterized joint optimization problem where compression efficiency and quality are controlled through unified parameter tuning during training
2Quantity of substance
If neural networks are trained to minimize rate and distortion disjointly with post-training quantization, then model compression is achieved, but encoding speed and representation efficiency deteriorate
Solution Approach 1:
The patent performs preliminary action by integrating all compression operations including quantization and entropy coding directly into the training process. The network is pre-trained with joint rate-distortion optimization and entropy constraints, eliminating the need for separate post-training quantization steps and subsequent heuristic compression operations
Solution Approach 2:
The patent maintains continuity of useful action by using a unified end-to-end training framework where rate and distortion optimization continue simultaneously throughout training. This continuous joint optimization replaces the discontinuous separate processing steps (training then post-processing) that cause encoding speed deterioration
3Loss of information
If existing neural video representation architectures are used, then video representation is achieved, but temporal and spatial coordinate compression efficiency is insufficient
Solution Approach 1:
The patent applies segmentation by dividing the video representation task into distinct spatial and temporal coordinate processing streams. The architecture separately encodes spatial coordinates and temporal coordinates through dedicated network paths, then combines their representations to achieve efficient compression of both dimensions without相互 interference
Data Source
AI summary
A system includes a neural network (NN) having a matrix expansion block configured to construct a matrix representation of an input sequence, a component merging block configured to merge the matrix representation with a grid, an encoder configured to receive an output of the component merging block, a convolution stage configured to generate, using an output of the encoder, a multi-component representation of an output corresponding to the input sequence, and a convolutional upscaling stage configured to produce, using the multi-component representation of the output, an output sequence corresponding to the input sequence. A method for use by the system includes receiving an input sequence, modeling the input sequence to generate a neural network representation of the input sequence, compressing the neural network representation to generate a compressed neural network representation, and generating, from the compressed neural network representation, a compressed output sequence corresponding to the input sequence.