Conditional Image Compression Using Segmented Neural Network Encoding
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Solution Overview
Problem
Conventional conditional image compression techniques face challenges with poor parallelization suitability and high memory demands, limiting their efficiency in encoding and decoding video and image data, especially with limited network resources and increasing demands for higher video quality.
Innovation Solution
The method involves encoding a primary component of an image independently from secondary components using neural networks, allowing for concurrent encoding and reduced memory demands, with processing in latent space that includes down-sampling and using entropy models for efficient bitstream generation and decoding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If conventional conditional image compression techniques are used, then compression ratio is improved, but parallelization suitability deteriorates and memory demands increase
Solution Approach 1:
The image is divided into multiple independent components (e.g., luma and chroma components in YUV color space). Each component is encoded separately using independent neural network encoders, enabling parallel processing while maintaining compression efficiency. The segmentation allows each component to be compressed independently without requiring complex inter-component dependencies.
Solution Approach 2:
The patent employs a universal encoding framework where multiple component encoders share a common architectural structure and can process different image components using the same methodology. This multi-functional approach allows the system to handle various components (luma, chroma, etc.) uniformly, improving parallelization while maintaining consistent compression performance across all components.
2Loss of information
If conventional conditional image compression techniques are used, then compression ratio is improved, but memory demands increase
Solution Approach 1:
By segmenting the image into independent components and encoding each separately, the patent reduces the amount of memory required to store intermediate representations. Each component encoder processes only its specific component data, avoiding the need to hold entire multi-component image data in memory simultaneously, thus reducing overall memory demands while maintaining compression effectiveness.
3Manufacturing precision
If conventional conditional image compression techniques are used, then image quality is maintained, but processing speed deteriorates
Solution Approach 1:
The patent segments the encoding process into independent parallel operations for each image component. This allows simultaneous processing of multiple components through separate neural network encoders, significantly increasing processing speed while maintaining the quality of each individual component through dedicated encoding optimization.
Solution Approach 2:
The patent applies encoding operations selectively to different image components based on their specific characteristics and importance. By focusing computational resources on critical components while using more efficient encoding for less critical ones, the system achieves high overall processing speed while maintaining necessary image quality standards.
Data Source
AI summary
A conditional coding of components of an image is described. A method of encoding at least a portion of an image is provided, which comprises encoding a primary component of the image independently from at least one secondary component and encoding the at least one secondary component of the image using information from the primary component. Further, it is provided a method of encoding at least a portion of an image, comprising providing a residual comprising a primary residual component for a primary component of the image and at least one secondary residual component for at least one secondary component of the image that is different from the primary component, encoding the primary residual component independently from the at least one secondary residual component and encoding the at least one secondary residual component using information from the primary residual component.