Multimedia Encoding Quality Control via Content Classification
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Solution Overview
Problem
Current multimedia encoding techniques fail to maintain consistent perceived quality across varying content types, as the same encoding parameters can result in different quality experiences due to content-specific properties, such as motion and texture, leading to suboptimal quality adjustments.
Innovation Solution
The proposed solution involves a method where multimedia data is classified into content classes based on perceived quality metrics, and encoding parameters are dynamically adjusted to achieve a desired constant perceptual quality. This includes re-encoding segments using adjusted parameters to refine the quality metric, ensuring that the encoded segments converge to a target quality level.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the same encoding parameters are used for all content types, then the encoding process is simple and fast, but the perceived quality varies across different content types
Solution Approach 1:
The encoding parameters are made dynamic rather than static. The system continuously monitors perceived quality metrics during encoding and adjusts parameters in real-time based on content characteristics, allowing the encoding process to adapt to different content types while maintaining consistent quality
Solution Approach 2:
A feedback loop is implemented where the encoded output is evaluated against target quality metrics, and the results are used to adjust encoding parameters for subsequent segments. This closed-loop control ensures quality consistency across varying content types
2Manufacturing precision
If encoding parameters are dynamically adjusted for each content type, then perceived quality consistency is improved, but the encoding process becomes more complex and time-consuming
Solution Approach 1:
The video sequence is divided into segments that are processed individually with content-specific parameters. By segmenting the content based on characteristics like motion and texture, the system can apply targeted encoding strategies without needing to complexly analyze the entire sequence at once
Solution Approach 2:
The system changes encoding parameters such as quantization step size, block size, and transformation type based on detected content characteristics. These parameter adjustments are made systematically according to content classifications, managing complexity through structured parameter variation
3Manufacturing precision
If encoding parameters are dynamically adjusted for each content type, then perceived quality consistency is improved, but the computational time increases
Solution Approach 1:
Content classification and initial parameter selection are performed before the main encoding process. By pre-analyzing content characteristics and determining appropriate parameters in advance, the system avoids time-consuming adjustments during the actual encoding of each segment
Solution Approach 2:
The system applies full dynamic parameter adjustment only to segments where it is most needed based on content characteristics. For segments with uniform or less sensitive content, simpler encoding strategies are used, reducing overall computational time while maintaining quality consistency where it matters most
Data Source
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AI summary
This disclosure describes techniques for controlling a perceived quality of multimedia sequences to try to achieve a desired constant perceptual quality regardless of the content of the sequences. In particular, an encoding device may implement quality control techniques to associate a sequence segment with a content "class" based on the content of the segment, determine a perceptual quality metric of the sequence segment, and adjust at least one encoding parameter used to encode the segment is encoded such that for the perceptual quality of the sequence segment converges to the desired quality.