Autoregressive Subnetwork Parallel Processing for Image Compression
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Solution Overview
Problem
The autoregressive subnetwork in state-of-the-art image compression architectures processes samples serially, leading to slow decoding times and inefficient utilization of GPU resources due to its serial nature.
Innovation Solution
Implementing a concurrent processing approach where multiple samples of the quantized latent representation are processed simultaneously using a designed kernel shape that allows for row-wise or column-wise processing, enabling efficient use of GPU resources and speeding up the decoding process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If serial processing is used for autoregressive subnetwork, then processing accuracy is maintained, but processing speed deteriorates
Solution Approach 1:
The patent divides the latent representation into multiple blocks and processes them in parallel using different autoregressive subnetworks. Each block is handled by a dedicated subnetwork, enabling concurrent processing and significantly improving both processing speed and coding efficiency while maintaining accuracy through structured segmentation of the data.
2Use of energy by moving object
If serial processing is used for autoregressive subnetwork, then computational simplicity is maintained, but GPU resource utilization deteriorates
Solution Approach 1:
The patent combines multiple autoregressive subnetworks to process different blocks of latent representation simultaneously. By merging multiple processing units that operate in parallel, the system achieves high GPU resource utilization and improved coding efficiency, with the combined architecture managing complexity through coordinated parallel processing.
3Productivity
If concurrent processing is implemented, then processing speed is improved, but implementation complexity increases
Solution Approach 1:
The patent segments the latent representation into blocks that can be processed concurrently by multiple autoregressive subnetworks. This segmentation enables parallel processing and improved coding efficiency while managing implementation complexity through a structured approach where each block is independently handled by a dedicated subnetwork.
Data Source
AI summary
Embodiments of the present disclosure provide a solution for data processing. A method for data processing is proposed. The method comprises: processing, during a conversion between data and a bitstream of the data, a first set of samples of a reconstructed latent representation of the data and a second set of samples of the reconstructed latent representation by using a model, the first set of samples being associated with a first sample of the reconstructed latent representation and the second set of samples being associated with a second sample of the reconstructed latent representation; and performing the conversion based on a result of the processing.