Image Compression Bit Allocation for Pixel-Block Quality Control
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Solution Overview
Problem
Cloud image rendering technologies waste computing power and result in poor image quality due to uniform lossy compression across all image regions, failing to meet user requirements.
Innovation Solution
Determine a sub bit rate for each pixel block based on its area and pixel count, adjusting bit allocation according to the picture quality level, which is derived from luminance variance, to enhance image quality and reduce computing waste.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If uniform lossy compression is applied to all image regions, then the image can be transmitted within limited channel bandwidth, but the image quality deteriorates and computing power is wasted
Solution Approach 1:
The patent applies different compression bit rates to different pixel blocks based on their luminance variance characteristics. Regions with low luminance variance (smooth areas) receive higher compression ratios, while regions with high luminance variance (detailed areas) receive lower compression ratios to preserve quality. This local differentiation resolves the contradiction by optimizing both computing power usage and image quality preservation in different regions simultaneously.
Solution Approach 2:
The patent dynamically adjusts the compression bit rate parameter for each pixel block based on its luminance variance. By changing the compression parameter locally rather than uniformly, the system achieves better overall image quality while reducing total computing power consumption, as smooth regions can be compressed more aggressively without perceptible quality loss.
2Productivity
If all regions are compressed at same bit rate, then the compression process is simple, but image quality is poor and computing power is wasted
Solution Approach 1:
The patent divides the image into multiple pixel blocks and calculates luminance variance for each block independently. This segmentation allows the system to identify regions with different compression requirements and apply appropriate bit rates to each, improving overall compression efficiency while maintaining image quality in critical regions.
Solution Approach 2:
Different compression bit rates are applied to different pixel blocks based on their local luminance variance characteristics. This local quality approach ensures that regions requiring higher quality preservation receive more bits, while smooth regions can be compressed more efficiently, resolving the contradiction between compression efficiency and image quality.
3Manufacturing precision
If high bit rate is used for all regions, then image quality is maintained, but channel bandwidth requirements increase and computing power is wasted
Solution Approach 1:
The compression bit rate parameter is changed dynamically for each pixel block based on its luminance variance. Regions with low variance can use higher compression ratios without quality loss, while regions with high variance use lower compression ratios. This parameter adaptation maintains image quality where needed while reducing computing power consumption in regions where it is not needed.
Data Source
AI summary
This application relates to an image processing method, apparatus, and system, and a storage medium, and relates to the field of video coding technologies. In this application, a server acquires a first image; then divides the first image into a plurality of pixel blocks and determines a sub bit rate of each pixel block; and finally compresses the first image based on a plurality of sub bit rates of the plurality of pixel blocks. Each pixel block includes a plurality of pixels in the first image, and the sub bit rate of each pixel block is determined based on an area of each pixel block and a quantity of pixels included in each pixel block.