Variational Autoencoder Sequence Compression for 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 via 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 per frame, thereby achieving high compression without losing reconstruction quality.
Solution Approach 2:
The patent changes the parameter representation by using neural network-trained encoder and decoder models that learn optimal parameter transformations. The encoder learns to map input sequences to compressed representations that preserve essential information, while the decoder learns to reconstruct high-quality sequences from these compressed parameters.
2Manufacturing precision
If conventional compression techniques are used to increase reconstruction quality, then compression rate decreases, but data size increases
Solution Approach 1:
By segmenting the sequence into static and dynamic components, the patent avoids compressing identical static information repeatedly. The static aspects are encoded once and reused, while only dynamic variations require full encoding, achieving high reconstruction quality with reduced data size.
Solution Approach 2:
The patent creates a compressed representation (encoding) of static aspects that can be copied and reused across multiple frames. This copying mechanism eliminates redundant data storage while maintaining reconstruction quality, as the same static encoding applies to all frames containing that static content.
3Quantity of substance
If conventional compression techniques are applied, then storage resources are reduced, but the limit of compression ratio is reached
Solution Approach 1:
The patent segments the compression process into static and dynamic handling, allowing extremely high compression for static portions (encoded once) while maintaining adequate representation for dynamic portions. This segmentation enables compression ratios beyond conventional limits by eliminating temporal redundancy.
Solution Approach 2:
The patent replaces conventional mechanical compression algorithms with neural network-based encoder and decoder models. These learned models achieve superior compression ratios by capturing complex patterns and relationships in the data that traditional algorithms cannot exploit, pushing beyond the compression limits of conventional techniques.
4Quantity of substance
If conventional compression techniques are applied, then network bandwidth requirements are reduced, but reconstruction quality deteriorates
Solution Approach 1:
The patent transforms the compression approach by using neural network models that learn optimal parameter transformations for efficient representation. The encoder learns to map sequences to compact parameter sets that preserve essential information, enabling high-quality reconstruction over limited bandwidth channels.
Solution Approach 2:
By encoding static aspects once and copying this encoding across multiple frames, the patent dramatically reduces the amount of data that must be transmitted over the network. Only dynamic variations need to be transmitted, achieving both bandwidth efficiency and high reconstruction quality.
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.