Adaptive Channel-Block Image Encoding for Variable Compression
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Solution Overview
Problem
Existing image compression methods using neural networks face challenges such as high computational complexity, restricted accuracy due to shared parameters, and inability to adapt to varying input images, leading to inefficient image encoding and decoding.
Innovation Solution
Adaptive selection of sub-neural networks based on channel blocks with predicted weights and compression rates, using a channel block prediction network to determine the number and importance of channel blocks for encoding and decoding images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If a neural network with many parameters is used for image compression, then image quality is improved, but computational complexity increases
Solution Approach 1:
The neural network is divided into multiple channel blocks that can be independently selected and processed. Instead of using the entire network with all parameters, the system segments the network into functional units (channel blocks) and activates only the necessary segments based on input image characteristics and compression requirements, thereby reducing computational complexity while maintaining image quality.
Solution Approach 2:
The system dynamically selects which channel blocks to activate based on the input image features and desired compression rate. This dynamic adaptation allows the network to adjust its computational complexity in real-time, using more blocks for high-quality requirements and fewer blocks for lower complexity scenarios, thus resolving the contradiction between image quality and computational load.
2Device complexity
If shared parameters are used in the neural network, then device complexity is reduced, but image quality accuracy is restricted
Solution Approach 1:
The network parameters are segmented into different channel blocks with potentially different parameter sets. This segmentation allows certain blocks to use shared parameters for efficiency while other blocks can use specialized parameters for accuracy, thus balancing device complexity and image quality without requiring the entire network to use unique parameters.
Solution Approach 2:
Different channel blocks can have different parameter configurations tailored to their specific functions. Instead of uniformly applying shared parameters across the entire network, the system applies local quality by allowing specific blocks to have specialized parameters where needed, while using shared parameters in blocks where generalization is sufficient, thus maintaining accuracy without excessive complexity.
3Device complexity
If a fixed neural network structure is used, then device complexity is reduced, but adaptability to varying input images is limited
Solution Approach 1:
The neural network structure becomes dynamic through the channel block selection mechanism. While the overall network architecture remains fixed (maintaining low device complexity), the active sub-structure adapts dynamically based on input image characteristics and compression rate requirements. This allows the system to adjust which channel blocks are activated, providing adaptability without requiring a completely reconfigurable network structure.
Solution Approach 2:
The system performs preliminary analysis of the input image to determine which channel blocks should be activated before actual encoding. This preliminary action allows the fixed network structure to be optimally configured for the specific input, enhancing adaptability without requiring the network structure itself to be complex or reconfigurable.
4Manufacturing precision
If all channel blocks are used for encoding, then image quality is improved, but processing time increases
Solution Approach 1:
The encoding process is segmented into multiple channel blocks that can be independently processed. Instead of sequentially processing all channel blocks, the system identifies and activates only the necessary blocks based on image characteristics and quality requirements, thereby reducing the total number of processing operations and encoding time while maintaining sufficient image quality through selective block usage.
Solution Approach 2:
The system applies partial action by using only the necessary subset of channel blocks required to achieve the desired image quality, rather than applying excessive action by processing all available blocks. This selective approach reduces processing time by avoiding redundant computations in blocks that would not significantly contribute to the final image quality at the given compression rate.
Data Source
AI summary
A processor-implemented method includes determining weights of a plurality of channel blocks comprised in a neural network to encode an image, selecting one or more channel blocks from the plurality of channel blocks to encode the image based on the weights and a compression rate of the image, and generating an encoded image by encoding the image using a sub-neural network comprising channels comprised in the one or more channel blocks.