Quadtree Bandwidth Compression Prediction for Video
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Solution Overview
Problem
Current video compression methods, particularly for complex texture images, face inefficiencies in prediction and entropy reduction, leading to poor prediction effects and high power consumption due to increased chip area and bus bandwidth requirements with higher video resolutions like 4K.
Innovation Solution
A quadtree-based bandwidth compression prediction method that recursively divides macroblocks into sub-macroblocks based on bit numbers and prediction residuals, using a quadtree algorithm to determine whether further division is necessary, thereby optimizing compression efficiency and subjective picture quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If video resolution is increased to 4K, then video quality is improved, but chip area cost and power consumption increase significantly
Solution Approach 1:
The patent applies quadtree segmentation to divide macroblocks into variable-size sub-macroblocks based on local complexity. This segmentation allows the prediction module to process only necessary regions at higher resolution, reducing overall computational load and power consumption while maintaining 4K video quality where needed.
Solution Approach 2:
The patent implements local quality adaptation by applying different prediction strategies to different regions of the video frame. Complex texture regions use advanced quadtree-based prediction while simpler regions use standard prediction, optimizing the balance between video quality and power consumption across different areas.
2Measurement precision
If video resolution is increased to 4K, then video quality is improved, but chip area cost increases
Solution Approach 1:
By segmenting the video processing into hierarchical levels using quadtree structures, the patent reduces the total number of processing elements needed. The segmentation enables shared resources across different resolution levels, decreasing chip area requirements while supporting 4K output quality.
Solution Approach 2:
The prediction module is designed with multi-functionality to handle both standard and complex texture regions using the same hardware infrastructure. The quadtree-based prediction structure serves multiple purposes: region classification, adaptive filtering control, and compression optimization, reducing the need for separate dedicated circuits.
3Device complexity
If texture feature analysis-based prediction is used, then processing is simple, but prediction effect and efficiency are poor for complex texture images
Solution Approach 1:
The patent introduces dynamic adaptation in the prediction process by using quadtree structures that adjust their depth and granularity based on local image characteristics. This dynamic approach allows the system to automatically increase processing complexity only where needed (complex texture regions) while maintaining simplicity in uniform regions, thereby improving overall prediction efficiency without excessive complexity.
Solution Approach 2:
The patent changes key parameters such as block size, prediction mode, and filtering strength based on local texture complexity detected through quadtree analysis. By dynamically adjusting these parameters rather than using fixed settings, the system achieves high prediction efficiency for complex textures while keeping the base processing framework relatively simple.
Data Source
AI summary
The present invention relates to a quadtree-based bandwidth compression prediction method and a system thereof. The bandwidth compression prediction method for example includes: dividing a to-be-predicted macroblock; obtaining a first prediction residual and a second prediction residual; judging whether re-dividing is performed on the to-be-predicted macroblock; and outputting the prediction residual and pixel component minimum values of the final sub-macroblocks divided from the to-be-predicted macroblock. According to the quadtree-based bandwidth compression prediction method and system, during the processing of complex texture images, the prediction effect is good, the processing efficiency is high, and the theoretical limit entropy can be reduced.