Predictive and Residual Coding for Sparse Weight Update Compression
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Solution Overview
Problem
Existing methods for compressing weight updates in neural network-based video codecs are inefficient for sparse signals, as they fail to effectively reduce the bitstream size due to the dense nature of residual signals.
Innovation Solution
A predictive and residual coding method with a switching mechanism that determines whether to transmit the residual, original signal, or coefficients based on signal fitness and rate distortion analysis to optimize communication efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If residual coding is applied to sparse signals, then compression efficiency is improved, but the residual signals become dense and increase bitstream size
Solution Approach 1:
The system dynamically switches between transmitting the original sparse signal and the residual signal based on real-time analysis of signal properties. A switching mechanism evaluates characteristics such as sparsity level and signal structure to determine which representation yields smaller bitstream size, allowing the system to adapt to varying signal conditions and resolve the contradiction between compression efficiency and bitstream size
Solution Approach 2:
The invention changes the representation parameter of the signal by transitioning between the original sparse signal domain and the residual signal domain. By analyzing signal fitness and rate distortion, the system selects the optimal parameter representation (original signal or residual) that minimizes bitstream size while maintaining compression efficiency
2Productivity
If predictive coding is used to approximate signals, then transmission efficiency is improved, but reconstruction accuracy may deteriorate
Solution Approach 1:
The system incorporates rate distortion analysis and signal fitness evaluation as feedback mechanisms to continuously monitor the quality of predictive coding approximations. Based on this feedback, the switching mechanism adjusts the transmission decision, selecting the representation that achieves the desired balance between transmission efficiency and reconstruction accuracy for each specific signal instance
3Device complexity
If a fixed coding method is used for all signals, then system complexity is reduced, but adaptability to different signal types deteriorates
Solution Approach 1:
The system employs a dynamic switching mechanism that adapts the coding method based on real-time signal characteristics analysis. Rather than using a fixed approach, the system evaluates signal fitness and rate distortion metrics to dynamically select between original signal transmission and residual transmission, achieving high adaptability while maintaining manageable system complexity through automated decision-making
Data Source
AI summary
An apparatus comprising: at least one processor; and at least one non-transitory memory storing instructions that, when executed by the at least one processor, cause the apparatus at least to: receive a signal, the signal comprising a sparse signal; perform residual coding on the signal; perform predictive coding on the signal; determine a residual, the residual comprising a residual of the signal and a base signal or a residual of an approximation and the base signal, the approximation being an approximation of the signal; and determine whether to transmit the residual or the signal over a communication channel.