Perceptual Video Coding via Region-Based Bit Allocation
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Solution Overview
Problem
Conventional video encoding methods face limitations in bandwidth efficiency, storage capacity, and processing delays, particularly when handling high-definition video streams, which can result in increased data rates and storage requirements, and transmission interruptions.
Innovation Solution
The implementation of a perceptual video coding module that downscales video streams spatially and temporally, using a partitioning module to identify regions of interest and allocate bits efficiently, while a rate control module manages bit distribution to optimize encoding and decoding processes, thereby reducing data rates and processing overhead.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If conventional video encoding methods are used to handle high-definition video streams, then video quality is maintained, but bandwidth efficiency deteriorates and data rates increase
Solution Approach 1:
The video stream is divided into multiple hierarchical levels (first level, second level, third level) based on spatial and temporal characteristics. Different regions are segmented into macroblocks and further into coding blocks, allowing selective encoding at different quality levels. This segmentation enables the system to transmit only essential information at high quality while compressing less important regions, thereby reducing overall data rate without significantly impacting perceived video quality.
Solution Approach 2:
The patent applies different encoding qualities to different regions of the video stream. Regions of interest (such as faces or important objects) are encoded at higher quality with more bits, while background regions are encoded at lower quality with fewer bits. This local quality differentiation is achieved through region-based partitioning and selective bit allocation, improving bandwidth efficiency by directing computational and transmission resources where they matter most.
2Productivity
If conventional video encoding methods process high-definition video streams, then encoding completeness is maintained, but processing time increases
Solution Approach 1:
The encoding process is segmented into multiple passes over the video data. In the first pass, the system performs coarser analysis and creates initial encoding decisions. Subsequent passes refine these decisions by processing finer details and adjusting bit allocation. This multi-pass segmentation allows the system to achieve high encoding completeness without requiring all processing to occur simultaneously, thereby reducing real-time processing time.
Solution Approach 2:
The system performs preliminary downscaling and initial region identification before final encoding. By pre-processing the video stream to create a simplified representation and identify key regions in advance, the system can make encoding decisions more efficiently. This preliminary action reduces the computational burden during actual encoding, improving processing speed while maintaining quality.
3Quantity of substance
If conventional video encoding methods are used, then encoding accuracy is maintained, but storage capacity requirements increase
Solution Approach 1:
The video data is segmented into hierarchical coding structures where essential information is preserved at higher levels and detailed information is stored at lower levels. This segmentation allows the system to store only the necessary precision for each region, reducing overall storage capacity requirements while maintaining encoding accuracy for critical content.
Solution Approach 2:
Different storage precision is applied to different regions based on their importance. Regions of interest are stored with higher precision and more bits, while background regions use lower precision with fewer bits. This local quality approach reduces total storage capacity requirements by avoiding uniform high-precision storage across the entire video stream.
4Reliability
If conventional video encoding methods handle high-definition video, then video fidelity is maintained, but transmission reliability deteriorates due to increased data rates
Solution Approach 1:
The video stream is segmented into priority-based groups where critical information is transmitted first and less critical information is transmitted later or at lower priority. This segmentation enables the transmission system to maintain reliability by ensuring delivery of essential data even under constrained bandwidth conditions, while still transmitting complete video content when bandwidth is available.
Solution Approach 2:
Different transmission priorities are assigned to different regions based on their visual importance. Regions of interest receive higher priority transmission with better error protection and quality assurance, while background regions use lower priority with reduced resource allocation. This local quality differentiation improves transmission reliability for critical content without requiring uniform high-reliability transmission across the entire stream.
Data Source
AI summary
A system for encoding a video stream into a processed video signal that includes at least one image. The system includes a downscaling module, a partitioning module, a rate control module, and an encoder section. The downscaling module receives the video stream and produces a downscaled video stream. A partitioning module, including a region detection module, receives the downscaled video stream and detects a pattern of interest in the at least one image. The partitioning module is operable to partition the at least one image based on the detected pattern of interest. The rate control module that receives an output from the partitioning module and produces an encoder control signal dependent on the output from the partitioning module. The encoder section, coupled to the rate control module, receives the video stream and generates the processed video signal.