Selective Image Restoration for Lossy Decoded Image Blocks
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Solution Overview
Problem
Deep learning-based image restoration techniques face challenges in reducing processing time while maintaining image quality, especially at higher compression rates, as deeper networks increase processing time and existing methods fail to improve image restoration performance by reducing the number of patterns in images.
Innovation Solution
A signal processing apparatus and method that decodes lossy compressed image data, where image restoration processing is performed selectively on blocks of the decoded image based on specific image information, using pre-learned coefficients to reduce processing time and enhance image quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If deeper neural networks are used to improve image restoration performance, then image quality is improved, but processing time increases
Solution Approach 1:
The patent divides the image into multiple blocks and performs restoration processing selectively on specific blocks based on their characteristics. This segmentation approach allows the system to apply complex restoration only where needed rather than processing the entire image uniformly, thus maintaining quality where required while reducing overall processing time.
Solution Approach 2:
The patent applies different processing strategies to different regions of the image based on local characteristics. By identifying blocks with specific features (such as those containing important visual information or those with higher degradation), the system concentrates computational resources on areas where restoration provides the most benefit, rather than applying uniform deep network processing to all image regions.
2Measurement precision
If the number of patterns to be learned is reduced to improve deep learning accuracy, then inferencing accuracy is improved, but the ability to handle diverse images is reduced
Solution Approach 1:
The patent performs preliminary classification of image blocks to identify their characteristics before applying restoration processing. By pre-analyzing block features (such as texture, frequency content, or importance metrics), the system can select appropriate pre-learned coefficient sets for each block type, achieving high accuracy without requiring the network to learn all possible image patterns from scratch.
Solution Approach 2:
The patent changes the parameters used for restoration processing based on the characteristics of each image block. Different coefficient sets are prepared for different block types, and the appropriate coefficients are selected based on local image properties. This allows the system to adapt to diverse image patterns while maintaining high inferencing accuracy for each specific pattern category.
Data Source
AI summary
A signal processing apparatus comprises a decoding unit configured to generate a decoded image by decoding lossy compressed image data, and a restoration processing unit configured to perform image restoration processing on the decoded image. The restoration processing unit determines whether or not to perform the restoration processing for each of blocks in the decoded image in accordance with specific image information, and for a block on which it is determined that the restoration processing is to be performed, performs the restoration processing on the basis of an inference made using a coefficient learned in advance.