Neural Network Data Compression with Nested Quantization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Traditional data compression techniques often require multiple models for different bitrates, leading to inflexibility and increased storage and transmission costs, as they are not dynamically adaptable to varying data types and needs.
Innovation Solution
A neural network-based method that uses progressive coding and nested quantization to compress data into a single model, allowing for dynamic control of compression bitrate by encoding content into a latent code space with varying quantization bin sizes, enabling encoding and decoding at multiple bitrates without needing multiple models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a fixed bitrate compression scheme is used, then the compression process is simple and deterministic, but the compression efficiency is suboptimal for varying data types and the quality cannot be dynamically adjusted
Solution Approach 1:
The patent applies dynamics by transitioning from a fixed bitrate compression scheme to a dynamic, adaptive compression system. The neural network encoder automatically adjusts the effective bitrate for different data types and complexity levels within the input data, enabling the compression ratio and quality to vary dynamically based on content characteristics rather than applying a uniform compression rate to all data
Solution Approach 2:
The patent changes the compression parameter from a fixed bitrate to a variable effective bitrate that is continuously adjusted based on data characteristics. The neural network modifies compression parameters adaptively, allowing the system to optimize compression efficiency for different types of content while maintaining a single unified compression model
2Manufacturing precision
If a higher bitrate is used for compression, then the decompressed quality is improved, but the storage and transmission costs increase
Solution Approach 1:
The patent applies local quality by allowing different regions or types of data within the input to be compressed at different effective bitrates. The neural network identifies and applies appropriate compression levels locally to different data characteristics, ensuring that important or complex regions receive higher quality compression while simpler regions use lower bitrates, optimizing the overall quality-to-size ratio
3Adaptability or versatility
If multiple separate compression models are used to support different bitrates, then adaptability to varying compression needs is improved, but the system complexity and number of models required increases
Solution Approach 1:
The patent implements universality by designing a single neural network compression model that can handle multiple compression scenarios and bitrate requirements. This unified model performs the functions of what would traditionally require multiple separate compression algorithms, adapting its behavior based on the input data characteristics and desired output quality without requiring separate specialized models for each compression level
Data Source
AI summary
Certain aspects of the present disclosure provide techniques for compressing content using a neural network. An example method generally includes receiving content for compression. The content is encoded into a first latent code space through an encoder implemented by an artificial neural network trained to generate a latent space representation of the content. A first compressed version of the encoded content is generated using a first quantization bin size of a series of quantization bin sizes. A refined compressed version of the encoded content is generated by scaling the first compressed version of the encoded content into one or more second quantization bin sizes smaller than the first quantization bin size, conditioned at least on a value of the first compressed version of the encoded content. The refined compressed version of the encoded content is output for transmission.