Texture And Motion ROI Detection For Adaptive Video Encoding
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Solution Overview
Problem
The rapid increase in video bitrate due to higher video quality demands poses challenges to network bandwidth costs and user viewing experience, and existing video encoding technologies lack efficient methods for detecting and encoding regions of interest, leading to inefficient bitrate allocation and hardware complexity.
Innovation Solution
A method for detecting regions of interest in images by dividing frames into mapping blocks, performing texture and motion region of interest detections, and determining the region of interest based on these results, which is implemented in hardware to support adaptive quantization and reduce computational complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If video quality is improved by increasing bitrate, then video quality is improved, but network bandwidth cost increases
Solution Approach 1:
The patent applies local quality by differentiating treatment between different regions of the video frame. Regions of interest (ROI) such as human faces and motion areas are identified and allocated higher bitrate to maintain high quality, while non-ROI areas use lower bitrate. This selective quality allocation improves overall perceived video quality while reducing total bandwidth consumption compared to uniform high-quality encoding.
Solution Approach 2:
The patent dynamically adjusts encoding parameters (quantization parameters, bitrate allocation) based on detected regions of interest. By changing these parameters selectively across different spatial regions and temporal frames, the system optimizes the balance between video quality and bandwidth consumption, avoiding the need for uniformly high bitrate across the entire video.
2Productivity
If traditional video encoding is used without region of interest detection, then encoding process is simple, but bitrate allocation is inefficient
Solution Approach 1:
The patent segments the video frame into multiple regions based on detected interests (human faces, motion areas, texture regions). Each segment is then independently encoded with appropriate bitrate allocation. This segmentation enables efficient bitrate distribution to important regions while maintaining manageable encoding complexity through modular processing of distinct regions.
Solution Approach 2:
The patent performs preliminary detection of regions of interest (faces, motion, texture) before the actual encoding process. This preliminary action identifies which areas require higher quality encoding, allowing the encoder to pre-allocate bitrate resources efficiently. By detecting ROIs in advance, the system avoids complex real-time adjustments during encoding while achieving optimal bitrate allocation.
3Measurement precision
If comprehensive region detection methods are used, then region of interest detection accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent segments the detection process into multiple specialized detectors: face detection, motion detection, and texture detection. Each detector focuses on specific features and operates independently on relevant regions. This segmentation improves detection accuracy by dedicating specialized algorithms to specific features while reducing overall computational complexity through parallel, modular processing rather than a single complex detector analyzing the entire frame.
Solution Approach 2:
The patent applies detection algorithms selectively to regions where they are most needed rather than uniformly across the entire video frame. For example, face detection is applied primarily to regions where faces are likely to occur, and motion detection focuses on areas with detected motion. This partial application of detection methods maintains high detection accuracy for critical regions while significantly reducing computational complexity by avoiding unnecessary processing in irrelevant areas.
Data Source
AI summary
The present disclosure relates to a method for detecting a region of interest in an image, a video encoding method, apparatuses, a computer device, a storage medium, and a computer program product. The method includes: obtaining a current to-be-detected image frame, determining a mapping block size parameter value from encoding parameters corresponding to the current to-be-detected image frame, dividing the current to-be-detected image frame to obtain multiple mapping blocks based on the mapping block size parameter value, performing a texture region detection on each mapping block to obtain a texture region of interest detection result. performing a motion region of interest detection on each mapping block to obtain a motion region of interest detection result, and determining a region of interest of the current to-be-detected image frame based on the texture region of interest detection result and the motion region of interest detection result.