Image Compression Quality Controller Bandwidth Optimization
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Solution Overview
Problem
Existing image data compression methods face challenges in efficiently managing bandwidth-limited connections, particularly in scenarios like virtual reality and videoconferencing, where high compression levels lead to loss of detail and low compression levels exceed bandwidth limits.
Innovation Solution
A method involving a quality controller that sets a target bit rate, determines a quantization level, and iteratively adjusts compression parameters to ensure the predicted number of bits does not exceed the desired target, using a relationship between bit rate and quantization level to optimize compression for image areas within an image frame.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If a high compression level is applied to image data, then bandwidth usage is reduced, but image quality and detail are lost
Solution Approach 1:
The patent divides the image frame into multiple image areas and applies different quantisation levels to different areas based on their importance and content characteristics. Important areas (e.g., containing motion or detail) receive lower quantisation levels to preserve quality, while less important areas receive higher quantisation levels to reduce bandwidth, thus resolving the contradiction between compression efficiency and image quality preservation.
2Loss of information
If a low compression level is applied to image data, then image quality is maintained, but bandwidth usage exceeds available limits
Solution Approach 1:
The patent dynamically adjusts the quantisation level parameter for different image areas based on a calculated bit rate model. By changing the quantisation parameter according to image content characteristics and bandwidth constraints, the system achieves optimal balance between maintaining image quality and staying within bandwidth limits, preventing both quality loss and bandwidth overflow.
3Device complexity
If uniform compression is applied to the entire image frame, then processing is simplified, but image quality is compromised in important areas
Solution Approach 1:
The patent segments the image frame into multiple image areas and processes each area with potentially different quantisation levels. This segmentation allows the system to apply higher compression to less important areas while preserving quality in important areas, achieving better overall image quality without excessive processing complexity through automated area classification.
Data Source
AI summary
Systems and methods for controlling compression of image data by a quality controller include obtaining a desired target number of bits to be generated from compression of a current image area using a predetermined compression protocol, determining a calculated quantisation level based on the desired number of bits using a predetermined relationship between the number of bits and quantisation level, selecting a discrete quantisation level from a plurality of predetermined discrete quantisation levels based on the calculated quantisation level, determining a predicted number of bits that would result from compression of the current image area at the selected discrete quantisation level using the predetermined relationship, determining whether the predicted number of bits exceeds the desired number of bits and, if not, providing to an encoder information to enable the encoder to determine a set of compression parameters associated with the selected discrete quantisation level.