Variational Autoencoder Sequence Compression With Static-Dynamic Encoding
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Solution Overview
Problem
Conventional compression techniques face a trade-off between high compression rates and high reconstruction quality, struggling to achieve both simultaneously, and have reached a limit in increasing compression ratios and reconstruction quality for multimedia data sequences.
Innovation Solution
Implementing encoder and decoder models generated through neural network training to identify and encode static aspects of input sequences, allowing for higher compression ratios and reconstruction quality by encoding a single instance of static aspects for the entire sequence.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If conventional compression techniques are used to increase compression rate, then data size is reduced, but reconstruction quality deteriorates
Solution Approach 1:
The patent segments the input sequence into static aspects (consistent throughout the sequence) and dynamic aspects (varying throughout the sequence). The encoder model processes these segments differently, encoding static aspects once and dynamic aspects locally, thereby achieving high compression while preserving reconstruction quality.
Solution Approach 2:
The patent changes the parameter representation by using neural network training to learn optimal encoding parameters for static aspects. The encoder model transforms the input sequence into a compressed representation that captures essential features, enabling both high compression ratios and high reconstruction quality through learned parameter transformations.
2Manufacturing precision
If conventional compression techniques are used to increase reconstruction quality, then compression rate decreases
Solution Approach 1:
By segmenting the sequence into static and dynamic aspects, the patent avoids compressing every element at full quality. Static aspects are encoded once with high fidelity, while dynamic aspects use differential encoding, achieving high overall reconstruction quality without proportionally increasing data size.
Solution Approach 2:
The patent applies partial action by focusing high-quality encoding only on static aspects that contribute most to overall reconstruction quality. Dynamic aspects use lighter encoding, achieving sufficient quality without the excessive data size that would result from applying full-quality encoding uniformly.
3Quantity of substance
If conventional compression techniques are used, then compression rate reaches a limit, but further compression is highly desirable
Solution Approach 1:
The patent replaces conventional mechanical compression algorithms with a neural network-based encoder model. This substitution enables the system to learn and exploit patterns in the data that traditional algorithms miss, achieving compression rates beyond conventional limits while maintaining reliability through the learned model's ability to generalize.
Solution Approach 2:
The patent changes the fundamental parameters of compression by using neural network training to learn optimal compression representations. The encoder model learns to map input sequences to compressed representations that capture essential information, breaking through the compression limits of conventional techniques while maintaining reconstruction reliability.
Data Source
AI summary
Embodiments include applying neural network technologies to encoding/decoding technologies by training and encoder model and a decoder model using a neural network. Neural network training is used to tune a neural network parameter for the encoder model and a neural network parameter for the decoder model that approximates an objective function. The common objective function may specify a minimized reconstruction error to be achieved by the encoder model and the decoder model when reconstructing (encoding then decoding) training data. The common objective function also specifies for the encoder and decoder models, a variable f representing static aspects of the training data and a set of variables z1:T representing dynamic aspects of the training data. During runtime, the trained encoder and decoder models are implemented by encoder and decoder machines to encode and decoder runtime sequences having a higher compression rate and a lower reconstruction error than in prior approaches.