Video Codec Quantization Constraints for Cross-Platform Reconstruction
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Solution Overview
Problem
In cross-platform video transmission, the accuracy of video frames obtained by decoding and reconstruction is relatively low due to inconsistencies in encoding and decoding processes performed by different computer devices.
Innovation Solution
A video encoding and decoding method that involves obtaining a training video frame, extracting a feature map, determining scale parameter values, mapping these values to a preset range, and updating model parameters based on quantization constraint losses to optimize video compression.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If standard video encoding and decoding models are used in cross-platform scenarios, then device compatibility is maintained, but video frame reconstruction accuracy deteriorates due to rounding boundary inconsistencies
Solution Approach 1:
The patent modifies the scale parameter values by adding an offset value during encoding, which shifts the parameter distribution away from rounding boundaries. This parameter transformation ensures that when decoding occurs on different platforms, the values remain consistent regardless of platform-specific rounding behaviors, thereby improving reconstruction accuracy while maintaining compatibility
Solution Approach 2:
The patent applies preliminary correction to the scale parameters during the encoding stage by adjusting them with an offset before quantization. This preliminary action prevents the rounding boundary inconsistency problem from occurring during decoding on different platforms, proactively solving the accuracy issue before cross-platform transmission occurs
2Reliability
If scale parameter values are constrained away from rounding boundaries, then quantization consistency across platforms is improved, but model complexity increases due to additional processing steps
Solution Approach 1:
The patent introduces a simple offset addition operation to shift scale parameter values away from rounding boundaries. This parameter change approach maintains reliability by ensuring consistent quantization behavior across platforms while adding minimal computational complexity - merely a simple arithmetic operation integrated into the existing encoding pipeline
Solution Approach 2:
The patent implements a feedback mechanism where the offset value is determined based on the distribution characteristics of scale parameters and their proximity to rounding boundaries. This feedback-driven approach automatically adjusts the offset to achieve optimal separation from rounding boundaries without requiring complex manual tuning or increasing overall system complexity
3Measurement precision
If quantization constraint loss optimization is applied, then video quality is improved, but encoding time increases due to additional loss calculation and model updating operations
Solution Approach 1:
The patent optimizes video quality by modifying the quantization process to use constraint loss that accounts for rounding boundary effects. By changing the parameter transformation approach (adding offset before quantization), the method improves effective video quality through more consistent reconstruction while the actual computational overhead remains limited to simple arithmetic operations integrated into the existing encoding flow
Data Source
AI summary
A video encoding and decoding method and apparatus for training models with quantization constraint optimization. The method obtains training video frames and extracts feature maps using a video encoding and decoding model. Scale parameter values are determined for each feature element and mapped to obtain scale parameter mapping values within a preset range. Constraint reference mapping values are obtained with distances to rounding boundary values greater than corresponding scale parameter mapping values. A quantization constraint loss is determined based on differences between scale parameter mapping values and constraint reference mapping values. Model parameters are updated using this quantization constraint loss to obtain a trained video encoding and decoding model with improved quantization performance and compression efficiency.