Neural Network Picture Encoding Resizing for Compression Efficiency
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Solution Overview
Problem
Current video coding techniques face challenges in achieving high compression ratios with minimal loss of picture quality, especially when dealing with limited bandwidth and storage resources, and existing neural network methods for encoding and decoding are not efficiently adaptable to signals of varying sizes.
Innovation Solution
A method using a neural network that involves resizing the input picture using various methods such as padding, cropping, or interpolation based on the downsampling ratios of the network's layers to optimize the processing size, allowing for efficient encoding and decoding while minimizing information loss.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If video data is compressed to reduce data size for transmission and storage, then the quantity of data is decreased, but picture quality is degraded
Solution Approach 1:
A neural network is introduced as an intermediary between the original picture and the compressed representation. The neural network learns optimal compression transformations during training, enabling efficient encoding that preserves more visual quality than traditional compression methods at the same bitrate. The network acts as a mediator that understands both the compression requirements and the perceptual importance of different picture regions.
Solution Approach 2:
The patent changes the parameters of the compression system by using learnable transformation parameters instead of fixed compression algorithms. The neural network dynamically adjusts compression parameters based on the content being encoded, allowing adaptive allocation of bits to different regions of the picture based on their visual importance, thereby maintaining quality while reducing data size.
2Adaptability or versatility
If neural network encoding is applied to pictures of varying sizes, then adaptability is improved, but computational complexity increases
Solution Approach 1:
The neural network architecture employs dynamic components that can adapt to different input sizes. The network uses techniques such as dynamic convolution kernels, adaptive pooling operations, and flexible layer configurations that automatically adjust based on the input picture dimensions. This dynamic behavior enables the same network to efficiently process pictures of varying sizes without requiring multiple specialized networks for each size category.
Data Source
AI summary
A method and an apparatus for encoding a picture and decoding a bitstream representing a matrix using a neural network is provided. The method includes obtaining a resizing method out of a plurality of resizing methods. The resizing method is applied to resize an input of size S to a size S. The resized input is processed by the neural network, wherein the neural network comprises one or more downsampling layers. Subsequently, the method provides an output of the neural network, the output having a size P that is smaller than S in the at least one dimension