Adaptive Video Transcoding via Content Complexity Analysis
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Solution Overview
Problem
Conventional video coding systems face challenges in optimizing video quality and computing cost when transcoding videos for hosting services, as they fail to efficiently allocate coding bits across different videos with varying content complexity, leading to inconsistent quality and user experience.
Innovation Solution
An adaptive transcoding system that uses a video coding complexity engine to measure encoding complexity, a rate-distortion modeling engine to estimate optimized bitrates and resolutions, and adaptive transcoding subsystems to adjust bitrates and resolutions based on video content, ensuring optimal visual quality and reduced computing costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a fixed encoding bitrate is applied to all videos, then the computing cost is reduced and processing is simplified, but the video quality becomes inconsistent and deteriorates for videos with varying content complexity
Solution Approach 1:
The patent applies local quality by assigning different encoding parameters (bitrate, resolution, frame rate) to different video segments based on their local complexity characteristics. The system analyzes motion magnitude, gradient magnitude, and texture frequency to identify complex and simple regions, then applies adaptive encoding strategies to each region to achieve optimal quality while managing computing cost.
Solution Approach 2:
The patent implements dynamics by making encoding parameters adaptive rather than fixed. The system dynamically adjusts bitrate, resolution, and frame rate based on real-time analysis of video content complexity metrics including motion magnitude, gradient magnitude, and texture frequency, allowing the encoding process to respond to varying content characteristics.
2Manufacturing precision
If conventional rate control algorithms are used to optimize bit allocation within a single video sequence, then the visual quality of individual videos is improved, but the allocation of coding bits among different videos in the corpus remains inefficient
Solution Approach 1:
The patent extends the traditional single-video rate control approach by adding a corpus-level dimension. The system evaluates video complexity metrics across the entire video corpus and uses this information to allocate total bitrate budget among multiple videos, then applies intra-video rate control based on local complexity analysis, creating a two-level optimization structure.
Solution Approach 2:
The patent applies parameter changes by modifying encoding parameters (bitrate, resolution, frame rate) based on quantified video complexity metrics. The system calculates complexity scores using motion magnitude, gradient magnitude, and texture frequency, then adjusts encoding parameters proportionally to achieve optimized bit allocation across the video corpus while maintaining quality standards.
3Quantity of substance
If a fixed small resolution is used for encoding simple videos such as slide shows and talking heads, then the storage and bandwidth requirements are reduced, but the video quality and user experience are significantly degraded
Solution Approach 1:
The patent applies local quality by determining the appropriate resolution for each video based on its specific content characteristics. The system analyzes motion magnitude, gradient magnitude, and texture frequency to classify videos as simple or complex, then assigns resolution levels accordingly - allowing simple videos like slide shows and talking heads to use lower resolutions while complex videos receive higher resolutions to maintain quality.
Solution Approach 2:
The patent implements parameter changes by dynamically adjusting resolution based on video complexity metrics. The system calculates complexity scores using motion magnitude, gradient magnitude, and texture frequency, then adapts the encoding resolution parameter to match the content requirements, avoiding both over-encoding of simple content and under-encoding of complex content.
4Device complexity
If the same target resolution and frame rate are applied to all videos, then the processing workflow is simplified and computing cost is reduced, but acceptable video quality cannot be achieved for every video
Solution Approach 1:
The patent implements dynamics by making target resolution and frame rate adaptive rather than fixed. The system dynamically determines appropriate encoding parameters based on analysis of video content complexity including motion magnitude, gradient magnitude, and texture frequency, allowing the processing workflow to adapt to different video types while maintaining quality standards.
Solution Approach 2:
The patent applies parameter changes by adjusting target resolution and frame rate based on quantified video complexity metrics. The system calculates complexity scores using motion magnitude, gradient magnitude, and texture frequency, then modifies encoding parameters (resolution, frame rate, bitrate) to match content requirements, achieving quality optimization without excessive workflow complexity.
Data Source
AI summary
A system and method provide a video coding system for adaptively transcoding videos based on video coding complexity (VCC). A VCC engine of the system is configured to generate a measure of how difficult to encode a source video based on a trained VCC model. A video rate-distortion modeling engine of the system is configured to estimate a rate-distortion model and a scaling model. The VCC model, rate-distortion model and the scaling model are trained on a video corpus of the system. The trained VCC model, rate-distortion model and the scaling model are used by an adaptive bitrate transcoding sub-system to transcode a source video with an optimized bitrate and visual quality. The trained VCC model, rate-distortion model and the scaling model are further used by an adaptive resolution transcoding sub-system to transcode a source video with an optimized resolution and visual quality.