Video Encoding Block Size Optimization via Relevance Analysis
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Solution Overview
Problem
Current video encoding schemes, such as H.264 and HEVC, require excessive processing power and memory due to exhaustive testing of various block sizes for optimal encoding, which is not viable for surveillance systems with limited resources and real-time encoding demands.
Innovation Solution
A method that determines the relevance of areas in an image frame and sets block sizes based on this relevance, allowing for parallel processing during pre-encoding stages, thereby reducing the number of encoding iterations and processing power required.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If exhaustive testing of all possible block size combinations is performed to achieve optimal encoding quality, then encoding quality is improved, but processing power requirements and encoding time increase excessively
Solution Approach 1:
The patent performs preliminary actions by analyzing the image frame before encoding to determine motion characteristics, noise levels, and detail content. This pre-analysis enables the system to pre-determine appropriate block size combinations without needing to exhaustively test all possibilities during the actual encoding process, thus reducing processing power requirements while maintaining encoding quality.
Solution Approach 2:
The patent changes the parameters of block size selection based on analyzed image characteristics. Instead of using fixed or exhaustive block size testing, the system adjusts block size parameters dynamically according to motion vectors, noise levels, and detail content detected in the image, achieving optimal quality with reduced processing power.
2Manufacturing precision
If multiple pass encoding is used to achieve high compression and image reproduction quality, then encoding quality is improved, but encoding time increases significantly
Solution Approach 1:
The patent performs preliminary analysis of the image frame to determine motion characteristics, noise levels, and detail content before encoding. This pre-computation of encoding parameters eliminates the need for multiple passes through the image data, as all necessary encoding decisions can be made in a single pass based on the preliminary analysis results.
Solution Approach 2:
The patent creates a simplified model or representation of the image characteristics (motion vectors, noise levels, detail content) that captures the essential information needed for encoding decisions. This model allows the system to make accurate encoding choices without repeatedly processing the entire image data multiple times.
3Quantity of substance
If multiple pass encoding is implemented to achieve high compression, then compression efficiency is improved, but memory and processing cycle requirements increase
Solution Approach 1:
The patent performs preliminary analysis to determine motion characteristics, noise levels, and detail content before encoding. This pre-computation stores essential encoding parameters that can be directly applied during single-pass encoding, eliminating the need for multiple passes and reducing memory and processing cycle requirements while maintaining compression efficiency.
Solution Approach 2:
The patent dynamically adjusts encoding parameters based on analyzed image characteristics such as motion vectors, noise levels, and detail content. This parameter adaptation enables efficient compression in a single pass without requiring multiple passes through the data, thereby reducing memory and processing cycle demands.
4Manufacturing precision
If block size selection is performed through exhaustive testing within each macroblock, then encoding optimization is improved, but processing time and computational complexity increase
Solution Approach 1:
The patent performs preliminary analysis of the entire image frame to determine motion characteristics, noise levels, and detail content before proceeding to encoding. This pre-analysis enables the system to pre-determine optimal block size combinations for each macroblock without needing to exhaustively test all possibilities during encoding, thus reducing computational complexity while maintaining optimization.
Solution Approach 2:
The patent applies different block size selection strategies to different regions of the image based on their local characteristics. Areas with high motion content, noise levels, or detail complexity receive different block size treatments compared to smooth or uniform regions, achieving localized optimization without exhaustive testing across the entire image.
Data Source
AI summary
The present invention relates to a system and a method for encoding a video stream. The encoding includes determining a level of relevance for areas in an image frame to be included in the video stream, determining a block size value for coding blocks in the image frame, the block size value is depending on the level of relevance determined for the area including each coding block, respectively, encoding the image frame using coding block sizes based on the determined block size values for each of the coding blocks.