Pixel Categorization for Visual Frame Compression Efficiency
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Solution Overview
Problem
Existing methods for compressing data representing visual frames are inefficient in distinguishing between lossless and lossy data, leading to suboptimal compression results due to the lack of effective pixel categorization and identification techniques.
Innovation Solution
The system detects and separates pixels within a frame by using a color manager module to create a color list and a single point identification module to categorize pixels as either lossless or lossy, allowing for targeted application of compression algorithms like JPEG for lossy data and RLE for lossless data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If existing compression methods are used without pixel categorization, then the compression process is simple, but compression efficiency and quality are suboptimal
Solution Approach 1:
The patent segments pixels into different categories (lossless and lossy) based on their properties such as color consistency and isolation. This segmentation allows different compression algorithms to be applied to different pixel types, improving overall compression efficiency while managing complexity through systematic classification.
Solution Approach 2:
The patent applies different compression qualities and algorithms to different regions or types of pixels. Lossless compression is applied to isolated or color-consistent pixels, while lossy compression is applied to other regions. This local differentiation optimizes compression efficiency for each pixel type while maintaining overall system manageability.
2Manufacturing precision
If all pixels are processed with the same compression algorithm, then the processing process is simple, but compression quality is suboptimal
Solution Approach 1:
The patent divides pixels into distinct segments (lossless and lossy categories) based on their visual properties. This segmentation enables the application of appropriate compression algorithms to each segment, significantly improving compression quality while controlling complexity through clear classification criteria.
Solution Approach 2:
The patent implements local quality by applying different compression treatments to different pixel regions. Isolated pixels or those with consistent colors receive lossless compression, while other pixels receive lossy compression. This localized approach optimizes quality for each region while maintaining processing manageability.
3Productivity
If pixel categorization is implemented, then compression efficiency improves, but processing time increases
Solution Approach 1:
The patent performs preliminary categorization of pixels into lossless and lossy groups before applying compression algorithms. This preliminary action, while adding some processing time, enables more efficient compression by avoiding unnecessary computations on already-categorized pixels and allowing parallel processing of different pixel types.
Solution Approach 2:
The patent applies lossless compression to only the necessary subset of pixels (isolated or color-consistent ones) rather than all pixels. This partial application of the more time-consuming lossless algorithm improves overall efficiency by avoiding excessive processing on pixels that can be handled by faster lossy compression.
Data Source
AI summary
The methods and apparatuses detect a plurality of pixels within a frame; separate a portion of the plurality of pixels into a lossless category based on inclusion of the portion of the plurality of pixels within a color list; and identify a particular pixel as an isolated pixel through a single point identification module wherein the particular pixel is within the portion of the plurality of pixels.