Video Encoding via CNN Scene Composition Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Encoding high-quality videos requires significant processing time and memory, making it difficult to share them efficiently, especially from mobile devices that captured the videos.
Innovation Solution
A system that uses a convolutional neural network to determine scene composition information from video visuals, allowing for intelligent encoding of video content by adjusting parameters such as quantization, block size, and bit distribution based on the scene composition, thereby reducing resource consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If high quality video encoding is performed, then video quality is improved, but processing time and memory requirements increase
Solution Approach 1:
The video encoding process is segmented into multiple passes: a first pass that performs lightweight scene composition analysis using a convolutional neural network to identify salient regions, and a second pass that applies high-quality encoding only to those identified regions. This segmentation allows the system to achieve high video quality while reducing overall processing time by avoiding exhaustive analysis of all video content.
Solution Approach 2:
The system applies different encoding qualities to different regions of the video based on scene composition analysis. High-quality encoding is applied to salient regions identified by the neural network (such as faces, objects of interest), while less critical regions use lower quality encoding. This local quality approach maintains overall video quality while significantly reducing processing time and memory requirements.
2Manufacturing precision
If high quality video encoding is performed, then video quality is improved, but memory requirements increase
Solution Approach 1:
The encoding process is divided into separate passes that operate with different memory requirements. The first pass uses the convolutional neural network for scene composition analysis with minimal memory usage, identifying salient regions. The second pass then applies high-quality encoding only to those regions. This segmentation prevents the system from needing to load and process the entire video at high quality simultaneously, thus reducing peak memory requirements.
Solution Approach 2:
By applying high-quality encoding only to locally identified salient regions rather than the entire video, the system reduces the total amount of data that needs to be held in memory during the encoding process. The convolutional neural network processes video frames sequentially to identify important regions, and only those regions are subjected to memory-intensive high-quality encoding operations.
3Productivity
If scene composition analysis is performed using convolutional neural network, then encoding efficiency is improved, but device complexity increases
Solution Approach 1:
The convolutional neural network acts as an intermediary component that bridges the gap between raw video input and the encoding process. It performs scene composition analysis by identifying salient regions and generating masks that guide the subsequent encoding stages. This intermediary layer enables encoding efficiency improvements without requiring the entire encoding system to be fundamentally complex, as the CNN can be integrated as a modular component.
Solution Approach 2:
The convolutional neural network performs preliminary scene composition analysis before the main encoding process begins. By pre-identifying salient regions and creating scene composition masks in advance, the system prepares guidance information that streamlines the subsequent encoding steps. This preliminary action reduces the complexity of the main encoding process, as the encoder can follow pre-determined guidelines rather than making complex decisions during encoding.
Data Source
AI summary
Video information defining video content to be encoded may be obtained. Scene composition information for the video content may be obtained. The scene composition information may be determined by a convolutional neural network based on visuals represented within the video content. The video content may be encoded based on the scene composition information. The encoding of the video content may generate encoded video information defining the encoded video content.