Region-Based Differential Image Encoding for High-Resolution Video
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Solution Overview
Problem
Current image encoding and decoding technologies face challenges in efficiently processing high-resolution and high-definition images, particularly in segmenting images into regions based on features and performing region-differential encoding and decoding to improve coding efficiency and reduce distortion.
Innovation Solution
The method involves generating a recovered low-quality image, segmenting the image into multiple regions, and performing encoding and decoding using different techniques for each region, with the option to use neural networks for differential recovery or distortion correction, depending on whether the image is low-resolution or low-bitrate.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional image encoding technology is used for high-resolution images, then compression is achieved, but coding distortion increases and coding efficiency decreases
Solution Approach 1:
The image is segmented into multiple regions based on features such as edges, textures, and semantic information. Different encoding strategies are applied to different regions: important regions (e.g., edges, semantic objects) use higher quality encoding with less compression, while less important regions use more aggressive compression. This segmentation allows the system to maintain high image quality where needed while achieving overall compression efficiency.
Solution Approach 2:
The patent applies local quality by using region-specific encoding parameters and techniques. Different quantization parameters, transform block sizes, and prediction modes are selected for different image regions based on their importance and characteristics. This ensures that critical image details are preserved with high fidelity while less critical areas are compressed more aggressively, resolving the contradiction between overall compression and local quality preservation.
2Productivity
If uniform encoding is applied to the entire image, then processing is simple, but coding efficiency for different regions is suboptimal
Solution Approach 1:
The patent performs preliminary actions by conducting feature detection, edge detection, and semantic segmentation before the main encoding process. These preliminary steps identify important regions and characteristics that guide subsequent region-specific encoding decisions. By preparing region masks and importance maps in advance, the system enables efficient region-differential encoding without excessive complexity during the main encoding phase.
Solution Approach 2:
The encoding parameters and strategies are dynamically adjusted for different regions based on their characteristics. The system automatically selects appropriate quantization parameters, block sizes, and prediction modes for each region, making the encoding process adaptive rather than static. This dynamic approach optimizes coding efficiency for each region while maintaining manageable processing complexity through automated decision-making algorithms.
3Loss of information
If high compression is applied to achieve better compression performance, then data transmission efficiency improves, but image quality and distortion reduction deteriorate
Solution Approach 1:
The patent changes encoding parameters dynamically based on region importance and characteristics. Different quantization parameters (QP), transform block sizes, and prediction modes are applied to different regions. Important regions use higher QP values (less compression) to preserve quality, while less important regions use lower QP values (more compression) to achieve better compression performance. This parameter variation resolves the contradiction by allowing high compression overall while maintaining quality where critical.
Data Source
AI summary
Disclosed herein are a video-decoding method and apparatus and a video encoding method and apparatus, and more particularly a method and an apparatus which perform region-differential image encoding/decoding using a recovered image. In accordance with an encoding method according to an embodiment, a recovered low-quality image is generated by performing encoding on an original image and a recovered high-quality image is generated using the recovered low-quality image. An image is segmented into multiple regions, and encoded reconstruction information for generating a reconstructed high-quality image is generated by performing encoding on the image.