Video Compression Unit Adaptive Buffer Budgeting
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Solution Overview
Problem
Existing video data compression methods face challenges in balancing compression rate and visual quality, particularly in ultra-high resolution videos, where lossy compression degrades quality and lossless compression is inefficient for complex textures, leading to suboptimal bandwidth usage and power consumption.
Innovation Solution
A method that adaptively allocates buffer space by calculating unit buffer budgets and compression costs for each compression unit, switching between lossless and lossy compression algorithms based on predefined thresholds, and adjusting quantization levels to maintain efficient buffer usage without degrading visual quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If lossy compression is used to increase compression rate, then bandwidth burden is reduced, but visual quality of video pictures is degraded
Solution Approach 1:
The patent applies local quality by differentiating compression strategies for different regions within video pictures. Specifically, it identifies important regions (such as regions containing motion vectors, macroblock addresses, or syntax elements) and applies lossless compression to these regions while allowing lossy compression in less critical areas. This selective approach maintains visual quality in important regions while achieving higher overall compression rates in non-critical regions, thus resolving the contradiction between compression rate and visual quality.
Solution Approach 2:
The patent segments the video picture into multiple compression units and processes each unit independently with different compression algorithms. By dividing the video data into smaller manageable blocks and applying appropriate compression methods to each segment based on its characteristics and importance, the system achieves both high compression rates and maintained visual quality where needed.
2Manufacturing precision
If lossless compression is used to maintain visual quality, then picture quality is preserved, but compression rate is low for video sequences with complex textures
Solution Approach 1:
The patent applies local quality by differentiating compression strategies for different regions within video pictures. Specifically, it identifies important regions (such as regions containing motion vectors, macroblock addresses, or syntax elements) and applies lossless compression to these regions while allowing lossy compression in less critical areas. This selective approach maintains visual quality in important regions while achieving higher overall compression rates in non-critical regions, thus resolving the contradiction between compression rate and visual quality.
Solution Approach 2:
The patent segments the video picture into multiple compression units and processes each unit independently with different compression algorithms. By dividing the video data into smaller manageable blocks and applying appropriate compression methods to each segment based on its characteristics and importance, the system achieves both high compression rates and maintained visual quality where needed.
3Manufacturing precision
If buffer space is increased to accommodate lossless compression, then visual quality is maintained, but external memory bandwidth and power consumption increase
Solution Approach 1:
The patent applies local quality by differentiating compression strategies for different regions within video pictures. Specifically, it identifies important regions (such as regions containing motion vectors, macroblock addresses, or syntax elements) and applies lossless compression to these regions while allowing lossy compression in less critical areas. This selective approach maintains visual quality in important regions while achieving higher overall compression rates in non-critical regions, thus resolving the contradiction between compression rate and visual quality.
Solution Approach 2:
The patent segments the video picture into multiple compression units and processes each unit independently with different compression algorithms. By dividing the video data into smaller manageable blocks and applying appropriate compression methods to each segment based on its characteristics and importance, the system achieves both high compression rates and maintained visual quality where needed.
Data Source
AI summary
A method for compressing video data, comprising: determining a compression target for a picture buffer; calculating a unit buffer budget for each compression unit based on the compression target, a size of the pixel array of the compression unit and a bit depth of pixels of the pixel array; receiving the video data; for a kth compression unit of each video picture of the video data: calculating a unit compression cost for the kth compression unit using a predetermined lossless compression algorithm and an accumulated compression cost, comparing the accumulated compression cost and an accumulated buffer budget for the kth compression unit, and selecting compressing the kth compression unit using the predetermined lossless compression algorithm or the predetermined lossy compression algorithm according to the comparison between accumulated compression cost and the accumulated buffer budget; and sending the compressed kth compression unit to the picture buffer.