Pixel Group Compression Balancing Ratio and Latency

Resolve Bottlenecks,
Find Innovative SolutionsGenerate Solutions

Solution Overview

Problem

Current compression techniques prioritize high compression ratios over processing latency, leading to inefficiencies in real-time image and video data analysis, particularly in mobile and distributed contexts where low latency and data integrity are critical.

Innovation Solution

The system compresses digital image data by independently compressing pixel groups, using comparison values to replace pixel data, allowing for dynamic adjustment of compression ratios based on latency and throughput requirements, and enabling parallel processing to minimize overall latency.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Loss of information

If large blocks of data are used for compression analysis to achieve higher compression ratios, then compression efficiency is improved, but processing latency and memory requirements increase

Engineering Contradiction:

Improvecompression ratioVSAvoidprocessing latency

Core Design Contradiction:

Loss of informationVSLoss of time

Solution Approach 1:

The image data is divided into multiple independent pixel groups (e.g., 16x16 or 32x32 pixel blocks) that can be compressed and transmitted independently. This segmentation allows the system to process smaller data units in parallel, reducing the waiting time for complete block reception while maintaining compression efficiency through selective application of compression algorithms on each segment.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system dynamically adjusts the compression strategy based on latency requirements. For time-sensitive applications, it uses smaller pixel groups with faster compression algorithms; for less time-sensitive applications, it can use larger blocks for higher compression ratios. This dynamic adaptation allows optimization of the trade-off between compression ratio and processing latency.

Inventive Principle:

Principle #15Dynamics

2Loss of information

If large blocks of data are used for compression analysis to achieve higher compression ratios, then compression efficiency is improved, but memory resources required increase

Engineering Contradiction:

Improvecompression ratioVSAvoidmemory resources

Core Design Contradiction:

Loss of informationVSQuantity of substance

Solution Approach 1:

By segmenting the image into smaller independent pixel groups, the system reduces the memory buffer required to hold complete large blocks before compression can begin. Each small pixel group can be processed with minimal memory overhead, enabling deployment on devices with limited memory resources while maintaining acceptable compression ratios.

Inventive Principle:

Principle #1Segmentation

3Loss of information

If compression processes wait for entire blocks of data before processing to maximize compression, then compression ratio is improved, but processing speed decreases

Engineering Contradiction:

Improvecompression ratioVSAvoidprocessing speed

Core Design Contradiction:

Loss of informationVSProductivity

Solution Approach 1:

The patent segments the image data into independent pixel groups that can be compressed as soon as they are received, without waiting for the entire image or large blocks. This enables pipelined processing where compression of early segments begins immediately, improving processing speed while maintaining compression efficiency on each segment.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system performs preliminary compression on received pixel groups before the complete image data is available. By starting compression on partial data immediately rather than waiting for complete blocks, the system reduces overall processing time while still achieving effective compression on the transmitted portions.

Inventive Principle:

Principle #10Preliminary action

4Loss of information

If complex compression algorithms are used to achieve higher compression ratios, then data transmission efficiency is improved, but processing complexity and latency increase

Engineering Contradiction:

Improvecompression ratioVSAvoidprocessing complexity

Core Design Contradiction:

Loss of informationVSDevice complexity

Solution Approach 1:

The system dynamically selects compression algorithm complexity based on application requirements. For real-time applications with strict latency constraints, it uses simpler, faster compression algorithms on small pixel groups. For applications where bandwidth is the primary constraint, it can employ more complex algorithms. This dynamic selection optimizes the balance between compression ratio and processing complexity.

Inventive Principle:

Principle #15Dynamics

Data Source

PatentUS11140420B2Methods and systems for balancing compression ratio with processing latency

Publication Date: 2021.10.05 PLEORA TECH

AI summary

Described are various embodiments of systems, methods and devices for transmitting, over a digital network, a digital image data object defined by a plurality of image pixels, wherein embodiments comprise: a digital image compressor operable to compress the digital image data object by independently compressing distinct pixel groups defined amongst the plurality of image pixels into independently compressed pixel groups to be transmitted over the digital network, in which, for each of said compressed pixel groups, a comparison value indicative of a similarity between given pixel data of a given group pixel and reference pixel data of a corresponding reference pixel is computed to at least partially replace said given pixel data; and a digital image decompressor coupled thereto operable to receive each of said independently compressed pixel groups for independent decompression.