Block-Based Image Filtering for Low-Distortion Video Coding
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Solution Overview
Problem
The increasing resolution and quality of image data lead to higher transmission and storage costs due to the larger amount of data required, necessitating high-efficiency image encoding/decoding technologies.
Innovation Solution
The use of filtering techniques based on artificial neural networks and matrix classification to reduce distortion in image encoding/decoding processes, including classification units and filtering performance information generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If high-resolution and high-quality image data is used, then image quality and resolution are improved, but transmission costs and storage costs increase
Solution Approach 1:
The image processing is divided into multiple filtering stages (first filtering, second filtering, third filtering) with different filter types applied to different blocks. This segmentation allows selective application of computationally intensive filters only where needed, reducing overall processing load while maintaining high image quality.
Solution Approach 2:
Different filter types are applied to different blocks based on their characteristics. The patent classifies blocks into various types (e.g., edge blocks, smooth blocks, texture blocks) and applies appropriate filters to each type, ensuring high image quality where needed while avoiding unnecessary processing in other areas.
2Manufacturing precision
If filtering is applied to reduce distortion, then image quality is improved, but processing complexity increases
Solution Approach 1:
The patent performs block classification before applying filters. By pre-categorizing blocks into different types based on their characteristics, the system determines which blocks require filtering and which type of filter to apply, avoiding unnecessary processing and reducing overall complexity.
Solution Approach 2:
The patent applies filtering selectively to only those blocks that benefit from it, rather than applying the same filter to the entire image. This partial action approach reduces processing complexity while maintaining image quality where it matters most.
3Manufacturing precision
If multiple filters are used for different blocks, then distortion reduction is improved, but processing time increases
Solution Approach 1:
The image is divided into multiple blocks, and different filter types are applied to different blocks based on their characteristics. This segmentation allows parallel processing of different blocks and avoids applying complex filters to blocks that don't need them, reducing overall processing time.
Solution Approach 2:
The patent changes filtering parameters (filter type, filter strength) based on block characteristics. By adapting parameters to local image features, the system achieves better distortion reduction with more efficient processing compared to applying a single fixed filter to the entire image.
Data Source
AI summary
Disclosed herein are a method, an apparatus, and a storage medium for image encoding/decoding using filtering. The image encoding method and the image decoding method perform classification on a classification unit of a target image, and perform filtering that uses a filter on the classification unit. A classification index of the classification unit is determined, and the classification index indicates a filter to be used for filtering on the classification unit, among multiple filters. The classification index is determined by various coding parameters, such as an encoding mode of the target block and an encoding mode of a neighboring block adjacent to the target block.