18455968. EFFICIENT NEURAL NETWORK MODULE FOR IMAGE COMPRESSION simplified abstract (TENCENT AMERICA LLC)
EFFICIENT NEURAL NETWORK MODULE FOR IMAGE COMPRESSION
Organization Name
Inventor(s)
Ding Ding of Washington DC (US)
Xiaozhong Xu of State College PA (US)
EFFICIENT NEURAL NETWORK MODULE FOR IMAGE COMPRESSION - A simplified explanation of the abstract
This abstract first appeared for US patent application 18455968 titled 'EFFICIENT NEURAL NETWORK MODULE FOR IMAGE COMPRESSION
Simplified Explanation
The patent application describes methods and apparatuses for neural network-based image compression. It involves generating predictions of input images using convolutional nets and activation functions, then decoding the compressed input image based on these predictions.
- Receiving a compressed input image
- Generating a prediction of the input image using convolutional nets and activation functions
- Decoding the compressed input image based on the generated prediction
Key Features and Innovation
- Utilizes neural networks for image compression
- Involves generating predictions of input images using convolutional nets and activation functions
- Decodes compressed input images based on these predictions
Potential Applications
- Image compression technology
- Data storage optimization
- Video streaming services
Problems Solved
- Efficient image compression
- Reduction of data storage requirements
- Improved video streaming quality
Benefits
- Enhanced image compression capabilities
- Reduced storage space needed for images
- Improved video streaming performance
Commercial Applications
Neural network-based image compression technology can be utilized in various industries such as telecommunications, media streaming services, and data storage solutions. This innovation can lead to more efficient data management and improved user experiences.
Questions about Neural Network-Based Image Compression
How does neural network-based image compression compare to traditional methods?
Neural network-based image compression typically offers higher compression rates and better image quality compared to traditional methods. This is due to the advanced algorithms and deep learning capabilities of neural networks.
What are the potential challenges in implementing neural network-based image compression on a large scale?
Implementing neural network-based image compression on a large scale may face challenges such as computational resources required for training the networks, ensuring real-time processing speeds, and optimizing the algorithms for different types of images and data sets.
Original Abstract Submitted
Methods and apparatuses for neural network based image compression may be provided. The method may include receiving a compressed input image; generating a first prediction of the input image using a first combination of one or more first convolutional nets, a first activation function, and the compressed input image, the generating includes at least: upsampling an output image from the one or more first convolutional nets; and performing tensor transform based on the upsampled output image; and decoding the compressed input image using the generated first prediction.
