Adaptive Quantization for Ultra-Low Latency Video Compression
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Solution Overview
Problem
Current image compression technologies for desktop applications face challenges in maintaining high image quality while achieving low latency, as they often compromise on quality to reduce latency or fail to effectively handle static and dynamic regions within desktop streams.
Innovation Solution
A codec architecture that employs motion-based adaptive quantization and adaptive bit rate control mechanisms to dynamically adjust compression quality for static and non-static regions, allowing for parallel processing and ultra-low latency compression by prioritizing quality in static regions and reducing it in dynamic regions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If conventional image compression is used to reduce latency, then transmission speed improves, but image quality deteriorates
Solution Approach 1:
The picture is divided into multiple regions based on motion characteristics. Static regions are identified and separated from dynamic regions, allowing different compression strategies to be applied to each region type, thereby achieving both low latency and high quality where needed
Solution Approach 2:
Different quantization parameters are applied to different regions of the picture. Static regions receive higher quality compression with better preservation of image details, while dynamic regions use lower quality compression to reduce overall bit rate and latency, optimizing the trade-off locally rather than globally
2Manufacturing precision
If compression quality is increased to maintain image fidelity, then image quality improves, but bit rate variability increases causing latency spikes
Solution Approach 1:
The system dynamically adjusts quantization parameters based on real-time detection of static and dynamic regions. This dynamic adaptation allows the encoder to respond to content changes, maintaining stable bit rate by applying appropriate compression strength to each region type rather than using a fixed high-quality setting throughout
Solution Approach 2:
Different quantization parameters are used for different regions - higher QP values for dynamic regions and lower QP values for static regions. This parameter differentiation stabilizes the overall bit rate by preventing excessive bit consumption in static areas while maintaining acceptable quality in dynamic areas
3Ease of operation
If uniform compression is applied to all regions, then processing simplicity is maintained, but static regions lose unnecessary quality
Solution Approach 1:
The picture is segmented into static and dynamic regions through motion detection and comparison with reference frames. This segmentation enables the system to apply differentiated compression strategies, preserving quality in static regions while maintaining simplicity through automated region classification
Solution Approach 2:
The encoder automatically detects and classifies regions as static or dynamic through motion analysis, then applies appropriate compression parameters without requiring manual intervention. The system serves itself by autonomously making compression decisions based on content analysis
Data Source
AI summary
A method and apparatus for enabling low latency compression of a stream of pictures are described. A first set of static regions of a current picture from the plurality of pictures is determined, where each region from the first set is static. A second set of regions of the current picture is determined, where the second set includes all regions of the current picture that are not included in the first set. Compression of the first set of regions is performed based on values of a first quantization parameter determined by a MAQ mechanism. The MAQ mechanism is operative to dynamically increase the compression quality of static regions. Compression of the second set of regions is performed based on values of a second quantization parameter determined by a rate control mechanism. The rate control mechanism is operative to compress the data stream according to a target bit rate.