INR Video Compression Using Differential Network Parameter Coding
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Solution Overview
Problem
Existing neural compression techniques, particularly Implicit Neural Representation (INR)-based methods, face challenges in optimizing computational complexity and bitstream efficiency due to the correlation of weights across successive video frames.
Innovation Solution
A differential coding mechanism is introduced to encode and decode the parameters of INR networks by utilizing the information from previously decoded frames, leveraging techniques like Neural Network Compression (NNC) and entropy coding to optimize bitstream efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If INR network parameters are encoded independently for each frame, then encoding simplicity is maintained, but bitstream efficiency deteriorates due to lack of exploitation of temporal correlations
Solution Approach 1:
The encoding process is segmented into two distinct parts: (1) encoding the first frame's INR parameters independently, and (2) encoding subsequent frames' parameter differences relative to the previous frame. This segmentation allows the system to exploit temporal correlations while maintaining manageable encoding complexity through modular processing.
Solution Approach 2:
The encoder performs preliminary encoding of the first frame's complete INR parameters before proceeding to encode only the differences for subsequent frames. This preliminary action establishes a reference that enables more efficient compression of temporal variations, reducing overall bitstream requirements while preserving encoding simplicity through a structured two-stage approach.
2Loss of information
If differential coding is applied to exploit temporal correlations, then bitstream efficiency is improved, but computational complexity increases
Solution Approach 1:
The encoding approach dynamically adapts to the temporal characteristics of the video sequence by switching between full parameter encoding (for the first frame) and differential encoding (for subsequent frames). This dynamic strategy exploits temporal correlations to improve bitstream efficiency while managing computational complexity through adaptive processing that responds to the actual content variations between frames.
Solution Approach 2:
Instead of encoding complete parameter sets for every frame, the system copies the previous frame's INR parameters and only encodes the differences (residuals). This copying approach significantly reduces bitstream requirements by leveraging temporal redundancy, while the computational complexity increase is limited to simple difference calculations rather than full parameter re-encoding.
3Measurement precision
If complete INR parameters are transmitted for each frame, then decoding accuracy is maintained, but data transmission volume increases
Solution Approach 1:
The system extracts only the essential information needed for reconstruction by transmitting complete INR parameters for the first frame and only the differences for subsequent frames. This extraction approach maintains decoding accuracy by preserving all necessary information while significantly reducing data transmission volume through selective transmission of parameter changes rather than redundant complete parameter sets.
Solution Approach 2:
The encoding strategy transitions from transmitting static complete parameter sets to transmitting dynamic parameter changes (differences) between frames. This parameter change approach maintains decoding accuracy by capturing temporal variations while reducing data transmission volume by eliminating redundant information, as the decoder can reconstruct full parameters by applying differences to the previous frame's parameters.
Data Source
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AI summary
In various implementations, method and devices are disclosed that encode or decode a sequence of images with an Implicit Neural network Representation INR. For instance, the encoding method comprises obtaining parameters of at least one current INR network for a current image of the sequence of images; obtaining parameters of at least one previous INR network for a previous image of the current image in the sequence of images; and encoding residual data between the parameters of the at least one current INR network and the parameters of the at least one previous INR network. According to different variant, the parameters may be quantized and/or dequantized before encoding. According to different variant, the parameter encoding uses entropy coding or neural network coding including quantization and deep CABAC.