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

arXiv: https://arxiv.org/abs/2510.08449 · HTML: https://mlsanity.com/paper/2510.08449/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 57 references that are in the map.
- First posted on arXiv 9 Oct 2025.

## Builds on (most cited first, 12 of 12)

- [Autoregressive Image Generation using Residual Quantization](https://mlsanity.com/paper/2203.01941/) (2022)
- [Towards Accurate Image Coding: Improved Autoregressive Image Generation with Dynamic Vector Quantization](https://mlsanity.com/paper/2305.11718/) (2023)
- [ANT: Exploiting Adaptive Numerical Data Type for Low-bit Deep Neural Network Quantization](https://mlsanity.com/paper/2208.14286/) (2022)
- [Regularized Vector Quantization for Tokenized Image Synthesis](https://mlsanity.com/paper/2303.06424/) (2023)
- [Self-supervised Product Quantization for Deep Unsupervised Image Retrieval](https://mlsanity.com/paper/2109.02244/) (2021)
- [Generalized Product Quantization Network for Semi-supervised Image Retrieval](https://mlsanity.com/paper/2002.11281/) (2020)
- [Exploration into Translation-Equivariant Image Quantization](https://mlsanity.com/paper/2112.00384/) (2021)
- [Efficient Neural PDE-Solvers using Quantization Aware Training](https://mlsanity.com/paper/2308.07350/) (2023)
- [Neural Networks with Quantization Constraints](https://mlsanity.com/paper/2210.15623/) (2022)
- [Vertical Layering of Quantized Neural Networks for Heterogeneous Inference](https://mlsanity.com/paper/2212.05326/) (2022)
- [Hybrid and Non-Uniform quantization methods using retro synthesis data for efficient inference](https://mlsanity.com/paper/2012.13716/) (2020)
- [Fixed-point quantization aware training for on-device keyword-spotting](https://mlsanity.com/paper/2303.02284/) (2023)

## Abstract (arXiv)

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., 51.50deg 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.