Laplacian Pyramid Decomposition for Hierarchical INR Video Coding
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Solution Overview
Problem
Existing neural compression techniques for 3D scenes and objects face challenges in computational complexity and efficiency, particularly in decomposing and reconstructing frames using hybrid implicit neural representations (INR) like COOL-CHIC, which struggle to effectively learn and process high-frequency and low-frequency components separately.
Innovation Solution
The method employs a Laplacian pyramid-based decomposition to separate frames into multiple resolutions, using a separate synthesis network for each level, and encodes the autoregressive model, latent grids, and synthesis network parameters into a bitstream, optimizing the loss function for improved reconstruction quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single synthesis network is used to process all frequency components, then the device complexity is reduced, but the manufacturing precision of frequency component separation deteriorates
Solution Approach 1:
The patent divides the image decomposition task into multiple levels using a Laplacian pyramid, where each level corresponds to a specific frequency range. Separate synthesis networks are trained for each level, allowing specialized processing of different frequency components. This segmentation enables each network to focus on specific frequency characteristics, improving reconstruction accuracy while maintaining manageable complexity through hierarchical organization.
2Manufacturing precision
If multiple separate synthesis networks are used for each Laplacian pyramid level, then the manufacturing precision of frequency component processing is improved, but the device complexity increases
Solution Approach 1:
The patent introduces a hierarchical dimension through the Laplacian pyramid structure, organizing multiple synthesis networks at different decomposition levels. This dimensional organization allows the system to process frequency components systematically from coarse to fine details, managing complexity through structured hierarchy rather than treating all networks as a flat collection.
3Productivity
If computational resources are increased for end-to-end neural compression, then the productivity of compression quality is improved, but the use of energy increases
Solution Approach 1:
The patent segments the compression task across multiple Laplacian pyramid levels, where each level processes a specific frequency range with dedicated synthesis networks. This segmentation allows computational resources to be distributed and optimized at each level rather than concentrating all computational demand in a single end-to-end network, reducing overall energy consumption while maintaining compression quality.
Data Source
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AI summary
Methods and apparatus are provided for Laplacian Pyramid based decomposition for feature-based implicit neural representation. In one embodiment, the COOL-CHIC method is used with a Laplacian Pyramid decomposition of a video frame into levels of varying resolutions for encoding of a video in intra mode. In another embodiment, COOL-CHIC is used with a Laplacian Pyramid for inter coding.