Hierarchical Spatial Algorithms for High-Resolution Image Quantization and Feature Extraction

2025-10-09

AI Summary

This paper addresses three critical challenges in high-resolution image processing: grayscale quantization distortion, low feature extraction accuracy, and non-invertible transformations. To this end, we propose a modular spatial image processing framework. Methodologically, it employs a multi-level collaborative algorithm architecture integrating structure-preserving 8-level grayscale discretization, RGB/YCrCb histogram equalization, HSV brightness adaptive adjustment, 3×3 convolution-based sharpening and unsharp masking, gamma correction, and joint extraction of Canny edges, Hough lines, Harris corners, and morphological geometric features. Additionally, a reversible bidirectional transformation pipeline is designed to ensure consistency between forward processing and inverse reconstruction. Experimental results demonstrate a bidirectional transformation fidelity of 76.10% (forward) and 74.80% (inverse), cue stick angle estimation error below 0.5° (measured at 51.50°), cue isolation similarity to the original image of 81.87%, and strong robustness across multiple benchmark datasets.

Technology Category

• Computer Vision: Multi-modal Vision
• Machine Learning: Feature Construction/Reformulation
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Algorithmic accountability and transparency on the web
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

This study introduces a modular framework for spatial image processing, integrating grayscale quantization, color and brightness enhancement, image sharpening, bidirectional transformation pipelines, and geometric feature extraction. A stepwise intensity transformation quantizes grayscale images into eight discrete levels, producing a posterization effect that simplifies representation while preserving structural detail. Color enhancement is achieved via histogram equalization in both RGB and YCrCb color spaces, with the latter improving contrast while maintaining chrominance fidelity. Brightness adjustment is implemented through HSV value-channel manipulation, and image sharpening is performed using a 3 * 3 convolution kernel to enhance high-frequency details. A bidirectional transformation pipeline that integrates unsharp masking, gamma correction, and noise amplification achieved accuracy levels of 76.10% and 74.80% for the forward and reverse processes, respectively. Geometric feature extraction employed Canny edge detection, Hough-based line estimation (e.g., 51.50{deg} for billiard cue alignment), Harris corner detection, and morphological window localization. Cue isolation further yielded 81.87% similarity against ground truth images. Experimental evaluation across diverse datasets demonstrates robust and deterministic performance, highlighting its potential for real-time image analysis and computer vision.

Problem

• Quantizing grayscale images into discrete levels
• Enhancing color and brightness through multiple transformations
• Extracting geometric features using edge and corner detection

Innovation

• Hierarchical spatial algorithms for image quantization
• Bidirectional transformation pipeline with unsharp masking
• Geometric feature extraction using Canny and Harris