Confidence-Based Video Encoding for ROI Bit Allocation
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Solution Overview
Problem
Conventional encoding methods for video signals suffer from latency, delays, and errors due to limitations in transmitting media, which affect the quality of bit streams and do not provide optimal bit allocation for regions of interest (ROIs) in video content.
Innovation Solution
The proposed solution involves confidence-based encoding that allocates bits to specific regions-of-interest (ROIs) using semantic similarity classification and confidence measures, dynamically determining the amount of additional video information for ROIs and less for non-ROIs, ensuring sharper and more detailed playback without requiring additional bandwidth.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional encoding methods are used to transmit video signals, then the bit stream can be transmitted through the transmitting media, but latency, delays, and errors are induced due to limitations of the transmitting media
Solution Approach 1:
The video signal is divided into multiple regions of interest (ROIs) based on semantic similarity classification. Each ROI is independently encoded with different bit allocation strategies, allowing critical regions to be transmitted with higher priority and better quality, while less important regions use compressed encoding. This segmentation enables selective optimization of transmission reliability for different parts of the video content.
Solution Approach 2:
Different encoding qualities are applied to different regions of the video frame based on their semantic importance. Regions with high semantic similarity to reference frames receive lower bit allocation, while regions with low semantic similarity (high confidence of change) receive higher bit allocation. This local quality differentiation maintains overall video quality while reducing total bandwidth requirements and transmission latency.
2Manufacturing precision
If additional video information is provided for regions of interest to improve perceptual quality, then sharper and more detailed playback is achieved, but bandwidth requirements increase
Solution Approach 1:
The encoding parameters (bit allocation, compression level, resolution) are dynamically changed based on semantic similarity confidence measures. For each block or region in the video, the encoder adjusts the quantization parameter and bit allocation according to the calculated confidence value, which reflects the semantic importance and change magnitude of that region. This parameter adaptation allows high detail quality for important regions while using aggressive compression for less important regions, optimizing the quality-bandwidth tradeoff.
Solution Approach 2:
Instead of uniformly encoding the entire video frame at high quality, the system applies partial high-quality encoding only to regions that genuinely require it (those with low semantic similarity and high confidence measures). The majority of the frame may use standard or reduced quality encoding, which is sufficient for perceptual acceptance. This partial action approach achieves the necessary detail quality for critical regions without the excessive bandwidth cost of full-frame high-quality encoding.
3Device complexity
If uniform bit allocation is used across the entire video frame, then encoding is simpler, but regions of interest do not receive sufficient detail and non-ROIs waste bandwidth
Solution Approach 1:
The bit allocation scheme transitions from static uniform allocation to dynamic region-specific allocation based on real-time semantic similarity analysis. The encoder continuously calculates confidence measures for different video blocks and adjusts bit allocation dynamically for each frame and even each block within a frame. This dynamic adaptation allows the system to automatically identify and prioritize regions of interest without manual intervention, achieving high region-specific quality with manageable complexity through automated semantic analysis.
Data Source
AI summary
The disclosure is related to allocation of bits in a media stream. In an example, a video stream is segmented into groups of pixels. A determination of a class type is made for individual ones of the groups of pixels. The determination can be based at least in part on semantic similarity of the class type and of a scene represented in the groups of pixels. A further determination occurs for sets of classified data associated with regions of interest (ROIs) according to the determined class type. Masking data associated with the sets of classified data is provided and confidence measures associated with the sets of classified data and the ROIs are determined. Bits are then allocated for groups of pixels based on the masking data and the confidence measures. Thereafter, a bit stream with the bits can be transmitted for playback on a computing device.