Neural Picture Coding with Rescaling for Low-Loss Compression
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Solution Overview
Problem
Existing video compression techniques face challenges in efficiently reducing the size of video data while maintaining high-quality reconstruction, especially with limited network resources and increasing demands for higher video quality.
Innovation Solution
A neural network framework that includes rescaling and downsampling layers to adjust input sizes before processing, minimizing unnecessary modifications and ensuring efficient compression without significant loss of information, and a corresponding upsampling and rescaling process for decoding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If video data is compressed to reduce data size for transmission or storage, then the quantity of data is decreased, but picture quality may be sacrificed
Solution Approach 1:
The patent applies preliminary rescaling to the input picture before downsampling to ensure that the picture dimensions are properly aligned with the downsampling ratio. This preliminary action prevents information loss and maintains picture quality by avoiding unnecessary modifications during the compression process.
Solution Approach 2:
The patent dynamically adjusts the rescaling factor based on the input picture size and downsampling ratio. By changing the parameter of picture dimensions to be a multiple of the downsampling ratio, the system optimizes compression efficiency while preserving picture quality through mathematically precise dimensional adjustments.
2Productivity
If downsampling is applied to reduce picture size, then data compression is improved, but information loss may occur if the picture size is not properly aligned with the downsampling ratio
Solution Approach 1:
The patent performs rescaling as a preliminary step before downsampling to align the picture dimensions with the downsampling ratio. This ensures that the downsampling operation can be applied efficiently without causing information loss, thereby maintaining both compression efficiency and information integrity.
Solution Approach 2:
The patent applies rescaling selectively based on the specific dimensions of the input picture and the required downsampling ratio. By adjusting only the necessary dimensional parameters to be multiples of the downsampling ratio, the system maintains local quality where needed while achieving global compression efficiency.
3Manufacturing precision
If rescaling is applied to adjust input size before downsampling, then information loss is minimized, but computational complexity increases
Solution Approach 1:
The patent calculates the rescaling factor by comparing input picture dimensions with the downsampling ratio and applying rescaling only when necessary. This parameter-based approach minimizes computational complexity by avoiding unnecessary rescaling operations while still preventing information loss when needed.
Solution Approach 2:
The patent applies rescaling partially - only to the extent necessary to align dimensions with the downsampling ratio. This partial action approach avoids excessive computational complexity by performing rescaling only when the input dimensions are not already suitable, rather than applying it universally to all cases.
Data Source
AI summary
Methods and systems for encoding a picture and decoding a bitstream that may represent an encoded picture. During encoding and decoding, rescaling operations are applied to rescale an input to a size that can be processed by a layer of a neural network. Embodiments disclosed herein provide methods for rescaling that achieve a reduced size of the bitstream, thereby improving compression.