Channel-Wise Autoregressive Entropy Models for Neural Image Compression
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current data compression methods are inefficient in achieving optimal rate-distortion performance and often require serial processing, limiting their effectiveness in compressing and decompressing data, particularly in image compression.
Innovation Solution
The implementation of a neural image compression architecture that combines channel-conditioning, latent residual prediction, and round-based training, utilizing encoder and decoder neural networks, hyperprior processing, and slice processing networks to generate and process quantized latent representations, resulting in improved rate-distortion performance and reduced serial processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current data compression methods are used, then data can be compressed and stored, but the rate-distortion performance is suboptimal and serial processing is required
Solution Approach 1:
The patent divides the image data into multiple slices that can be processed in parallel. Each slice is independently encoded using separate encoder neural networks, allowing simultaneous processing of multiple image regions. This segmentation eliminates the serial processing bottleneck while maintaining optimal rate-distortion performance through independent optimization of each slice's compression parameters.
Solution Approach 2:
The patent employs multiple encoder neural networks with different architectural parameters and configurations to process different slices. By varying network depth, width, and hyperparameters across parallel encoders, the system achieves diverse rate-distortion optimization strategies simultaneously, improving overall performance without serial dependency.
2Loss of information
If context-adaptive models are used for image compression, then better compression ratios can be achieved, but increased serial processing is required
Solution Approach 1:
The patent segments the image into multiple independent slices, each processed by dedicated context-adaptive encoder networks running in parallel. This allows context-adaptive compression to be applied to each slice independently and simultaneously, achieving high compression ratios without the time loss associated with sequential processing of the entire image.
Solution Approach 2:
The patent performs preliminary slicing and parallel encoding setup before compression begins. By pre-configuring multiple encoder networks and dividing the image into processable slices in advance, the system enables concurrent context-adaptive compression across all slices, reducing total processing time while maintaining high compression efficiency.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for channel-wise autoregressive entropy models. In one aspect, a method includes processing data using a first encoder neural network to generate a latent representation of the data. The latent representation of data is processed by a quantizer and a second encoder neural network to generate a quantized latent representation of data and a latent representation of an entropy model. The latent representation of data is further processed into a plurality of slices of quantized latent representations of data wherein the slices are arranged in an ordinal sequence. A hyperprior processing network generates a hyperprior parameters and a compressed representation of the hyperprior parameters. For each slice, a corresponding compressed representation is generated using a corresponding slice processing network wherein a combination of the compressed representations form a compressed representation of the data.