Object-Based Image Encoding for Bandwidth-Constrained Video
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Solution Overview
Problem
Video conferencing applications using mobile devices often experience suboptimal video quality due to lower bandwidth, leading to poor user experience, as existing image processing technologies do not efficiently manage bit density and object prioritization in image frames.
Innovation Solution
An image processing system that includes an encoding engine to compress objects of interest with higher bit densities than the background within image frames, and a context engine to identify and scale regions of interest, allowing for separate transmission and emphasis of objects such as faces, improving video quality and user experience, especially in bandwidth-constrained environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If uniform bit density is used to compress all regions in image frames, then compression is simple to implement, but video quality deteriorates in bandwidth-constrained environments
Solution Approach 1:
The patent applies different bit densities to different regions within the same image frame. Objects of interest are encoded with higher bit densities to preserve quality, while background regions use lower bit densities for efficient compression. This local differentiation resolves the contradiction by maintaining video quality where it matters most while accepting lower quality in less important areas.
Solution Approach 2:
The image frame is segmented into multiple regions with different importance levels. The encoding system divides the frame into objects of interest and background regions, applying separate compression parameters to each segment. This segmentation allows the system to optimize for both quality and compression efficiency simultaneously.
2Reliability
If higher bit density is used to compress objects of interest, then video quality of important regions improves, but total data transmission volume increases
Solution Approach 1:
Instead of uniformly increasing bit density across the entire frame, the patent applies higher bit density only to specific local regions containing objects of interest. This localized approach improves the quality of important regions while keeping the data volume increase minimal, as background regions continue to use lower bit densities.
Solution Approach 2:
The patent applies compression effort selectively rather than uniformly. By concentrating computational resources and bit allocation only on portions of the image that contain objects of interest, the system achieves improved quality where it matters most without the excessive data volume increase that would result from applying high bit density to the entire frame.
3Device complexity
If all regions in image frames are compressed with equal priority, then compression process is simple, but user experience deteriorates when bandwidth is limited
Solution Approach 1:
The compression system dynamically adjusts bit density allocation based on the importance of different regions in each frame. Rather than using static uniform compression, the system adapts its compression parameters frame-by-frame, identifying objects of interest and allocating more bits to those regions while reducing bits for background areas, thereby improving user experience without excessive complexity.
Solution Approach 2:
The patent changes the compression parameters (bit density) based on the spatial distribution of important content in each frame. By varying the compression parameter across different regions and frames according to content importance, the system improves user experience in bandwidth-constrained conditions while maintaining manageable process complexity through automated parameter adjustment.
Data Source
AI summary
Various embodiments of this disclosure may describe apparatuses, methods, and systems including an encoding engine to encode and/or compress one or more objects of interest within individual image frames with higher bit densities than the bit density employed to encode and/or compress their background. The image processing system may further include a context engine to identify a region of interest including at least a part of the one or more objects of interest, and scale the region of interest within individual image frames to emphasize the objects of interest. Other embodiments may also be disclosed or claimed.