Single Neural Network Video Compression with Dynamic Bitrate Masking
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Solution Overview
Problem
Current neural network-based video compression methods require multiple networks for different compression rates, leading to increased memory requirements and inefficiencies, as they cannot tune bitrate/accuracy effectively.
Innovation Solution
A single neural network is trained with different compression rates using masking techniques, allowing it to compress and decompress data efficiently by removing or padding elements based on determined compression/decompression rates, thereby eliminating the need for multiple networks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple neural networks are used for different compression rates, then each compression rate can be optimized, but memory requirements and system complexity increase
Solution Approach 1:
The patent combines multiple compression rate optimizations into a single neural network by integrating a compression rate determination module that selects appropriate compression rates dynamically. This merging approach maintains the optimization benefits of multiple networks while reducing system complexity and memory requirements by using one unified network architecture.
Solution Approach 2:
The single neural network is designed to handle multiple compression rates through a universal architecture that includes a compression rate determination module. This module can adaptively select and apply different compression rates based on input characteristics, making the single network perform the function of multiple specialized networks.
2Device complexity
If a single neural network is used for all compression rates, then memory requirements are reduced, but the ability to tune bitrate/accuracy effectively is limited
Solution Approach 1:
The patent introduces dynamic adaptability into the single neural network through a compression rate determination module that can adjust compression rates based on input data characteristics. This dynamic mechanism allows the network to effectively tune bitrate and accuracy for different scenarios, overcoming the limitation of fixed compression rate approaches.
Solution Approach 2:
The system changes the compression rate parameter dynamically based on input characteristics and performance requirements. By allowing this key parameter to vary rather than remain fixed, the single neural network achieves effective bitrate and accuracy tuning comparable to multiple specialized networks.
3Productivity
If compression rate is increased, then data transmission efficiency improves, but output data quality decreases
Solution Approach 1:
The compression rate determination module dynamically adjusts the compression rate based on the specific characteristics of the input data and the desired output quality. This dynamic adjustment allows the system to achieve high transmission efficiency when appropriate while maintaining output quality when necessary, optimizing the trade-off between these two parameters.
Solution Approach 2:
The system changes the compression rate parameter adaptively rather than using a fixed value. By adjusting this parameter based on real-time conditions and requirements, the system can maximize transmission efficiency while maintaining acceptable output quality, or prioritize quality when needed.
Data Source
AI summary
The embodiments relate to a method comprising compressing input data (I) by means of at least a neural network (E, 310); determining a compression rate for data compression; miming the neural network (E, 310) with the input data (I) to produce an output data (c); removing a number of elements from the output data (c) according to the compression rate to result in a reduced form of the output data (me); and providing the reduced form of the output data (me) and the compression rate to a decoder (D, 320). The embodiments also relate to a method comprising receiving input data (me) for decompression; decompressing the input data (me) by means of at least a neural network (D, 320); determining a decompression rate for decompressing the input data (me); miming the neural network (D, 320) with input data (me) to produce a decompressed output data (ĩ); padding a number of elements to the compressed input data (me) according to the decompression rate to produce an output data (ĩ); and providing the output data (ĩ).