Parallel Video Encoding via Scene Segmentation
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Solution Overview
Problem
The increasing computational intensity of advanced video codecs, such as AVC, leads to longer video encoding times, and the conventional multiple encoding passes approach further prolongs this process due to dependencies between passes, resulting in inefficient use of encoding resources and reduced throughput.
Innovation Solution
The method involves segmenting video content into scenes, detecting and dissecting long scenes into segments, and encoding these segments in parallel across multiple nodes or clusters, optimizing the use of encoding resources without sacrificing quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If advanced video codecs (such as AVC) are used to compress video more effectively, then bit rate efficiency and video quality are improved, but the computational intensity increases and encoding time is prolonged
Solution Approach 1:
The video content is segmented into multiple independent scenes based on scene detection algorithms. Each scene is then encoded independently in parallel, allowing the encoding process to utilize multiple processing nodes simultaneously. This segmentation enables time-optimized encoding by eliminating sequential dependencies while maintaining the quality benefits of advanced codecs.
2Manufacturing precision
If multiple encoding passes are used to improve encode quality, then encoding quality is enhanced, but the encoding time is undesirably increased due to dependencies between passes
Solution Approach 1:
The video stream is divided into multiple scenes that can be processed independently. Multiple encoding passes can be applied to different scenes simultaneously in parallel, rather than sequentially processing the entire video through each pass. This maintains encode quality while significantly improving throughput.
Solution Approach 2:
The patent transitions from a single-dimensional sequential processing model to a multi-dimensional parallel processing model. By introducing the dimension of parallel scene processing, multiple encoding passes operate concurrently across different scenes, transforming the time-quality tradeoff into a spatial parallelization opportunity.
3Device complexity
If single piece of encoding hardware is used with simple codecs, then the system is simple and encoding is fast, but bit rate efficiency and video quality are insufficient
Solution Approach 1:
The video encoding task is segmented into multiple independent scene encoding tasks that can be distributed across multiple processing nodes. This allows the system to scale from simple single-node encoding to complex multi-node parallel encoding, achieving high video quality with advanced codecs while managing complexity through modular scene-based processing.
Data Source
AI summary
The present disclosure is directed towards a system and method for time optimized encoding. Time optimized encoding maximizes the usage of the multiple encode nodes or clusters by parallelizing time-intensive encoding with minimal or no sacrifice of encoding quality. Thereby, reducing the time required for accomplishing the time intensive encoding and increasing the efficient throughput of multiple encodes through the system.