Multimedia Encoding Resource Prediction via Supervised Learning
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional multimedia content encoding in the cloud faces inefficiencies due to oversizing or undersizing of resources, leading to wasted resources or decreased video quality, as existing methods rely solely on resolution-based allocation without considering various content characteristics and encoding parameters.
Innovation Solution
A supervised learning algorithm, such as a neural network, is employed to predict and optimize resource allocation for multimedia content encoding based on specific content characteristics and encoding parameters, allowing for individualized optimization of resource usage and continuous training for improved resource management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If resource allocation is based solely on resolution (SD, HD, UHD), then the allocation process is simple, but resources are either oversized (wasting resources) or undersized (decreasing video quality or taking more time)
Solution Approach 1:
The patent changes the parameters used for resource allocation from simple resolution-based categories to a comprehensive set of parameters including content characteristics (complexity, motion intensity, scene changes), encoding parameters (codec type, bitrate, resolution), and desired output quality. This multi-parameter approach enables precise resource estimation that matches actual encoding needs, resolving the contradiction between allocation simplicity and encoding efficiency.
Solution Approach 2:
The system performs preliminary analysis of content characteristics and encoding requirements before actual resource allocation. By pre-processing the input to understand content complexity, motion patterns, and scene changes, the system can accurately predict resource needs in advance, avoiding both resource waste and quality degradation during the encoding process.
2Ease of manufacture
If resource allocation is based solely on resolution (SD, HD, UHD), then the allocation method is easy to implement, but video quality may decrease or encoding time increases
Solution Approach 1:
The patent introduces additional parameters beyond resolution, including content complexity metrics, motion intensity, scene change frequency, and codec-specific requirements. These parameters are integrated into the resource allocation model to ensure that video quality requirements are met while optimizing encoding time, thus maintaining reliability without sacrificing ease of implementation.
Solution Approach 2:
The system incorporates feedback mechanisms where actual encoding performance data is used to refine resource allocation predictions. By continuously learning from encoding outcomes, the system improves its ability to maintain consistent video quality across different content types while preserving the simplicity of the allocation method.
3Productivity
If more resource parameters are considered for optimization, then resource usage is individually optimized, but the complexity of determining resource allocation increases
Solution Approach 1:
The patent introduces an intermediary computational model that processes multiple input parameters (content characteristics, encoding parameters, quality requirements) and outputs optimized resource allocation decisions. This intermediary layer simplifies the complexity by providing a structured framework for integrating numerous parameters without requiring direct complex interactions between all parameters, thus maintaining high optimization accuracy while managing system complexity.
Data Source
Figure 1
Figure 2a
Figure 2b
AI summary
A method for managing encoding of multimedia content stored in a file is proposed, which comprises: determining, using a supervised learning algorithm, a prediction of processing resources required for encoding the multimedia content, based on one or more multimedia content characteristics of the multimedia content and on one or more multimedia content encoding parameters for encoding the multimedia content; and determining a processing configuration for encoding the multimedia content based on the prediction of processing resources.