Image Compression Using Normalizing Flows for Latent Space Modeling
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Solution Overview
Problem
Conventional neural image compression methods face challenges in finding powerful encoder/decoder transformations and properly modeling the distribution in the latent space, particularly in achieving a balance between compression rate and distortion.
Innovation Solution
The use of normalizing flows, which involve a series of invertible mappings to transform a simple probability distribution into a more complex one, enabling effective modeling of the latent space distribution and improving the rate-distortion trade-off in image compression.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If conventional neural network architectures are used for encoding and decoding, then good compression results can be achieved, but the ability to accurately model the distribution in the latent space remains insufficient
Solution Approach 1:
The patent changes the parameterization approach by using normalizing flows with learnable transformation parameters instead of conventional neural network parameters. This allows the latent space distribution to be modeled more accurately through the flow transformation parameters, resolving the contradiction between compression rate and distribution modeling accuracy
Solution Approach 2:
The patent substitutes the conventional neural network mechanism with a normalizing flow mechanism. The normalizing flow provides a more reliable probabilistic model for the latent space while maintaining compression effectiveness, replacing the less accurate conventional approach
2Reliability
If the focus is on accurately modeling the distribution in the latent space, then better probability estimation is achieved, but compression performance may be compromised
Solution Approach 1:
The patent merges the distribution modeling function with the compression function by using normalizing flows that simultaneously provide accurate probability estimation and effective compression. The flow-based approach unifies these two objectives rather than treating them as separate competing goals
Solution Approach 2:
The normalizing flow architecture serves multiple functions: it models the latent space distribution accurately, enables entropy coding for compression, and provides a probabilistic framework for rate-distortion optimization. This multi-functionality resolves the contradiction by making the system effective at both distribution modeling and compression
3Adaptability or versatility
If autoencoder-based methods are used, then encoding and decoding can be performed, but the quality levels are limited and reconstruction consistency varies
Solution Approach 1:
The patent introduces dynamic adaptability through the normalizing flow framework, which can adjust the transformation parameters to achieve different quality levels from low bitrate to near lossless. The learnable flow parameters enable the system to dynamically optimize for different reconstruction quality requirements
Solution Approach 2:
The patent employs feedback through the probabilistic modeling of the latent space, where the learned distribution provides information about reconstruction quality and uncertainty. This feedback mechanism enables consistent reconstruction by optimizing the rate-distortion tradeoff based on the modeled distribution
Data Source
AI summary
According to one implementation, an image compression system includes a computing platform having a hardware processor and a system memory storing a software code. The hardware processor executes the software code to receive an input image, transform the input image to a latent space representation of the input image, and quantize the latent space representation of the input image to produce multiple quantized latents. The hardware processor further executes the software code to encode the quantized latents using a probability density function of the latent space representation of the input image, to generate a bitstream, and convert the bitstream into an output image corresponding to the input image. The probability density function of the latent space representation of the input image is obtained based on a normalizing flow mapping of one of the input image or the latent space representation of the input image.