About

This tool is a comprehensive client-side image signal processor (ISP) designed for developers, designers, and computer vision researchers. Unlike standard photo editors that rely on opaque presets, this utility exposes the mathematical foundations of image manipulation - specifically Discrete Convolution, Color Matrix Transformations, and Error Diffusion Dithering.

By operating directly on the RGBA Uint8ClampedArray of the HTML5 Canvas, it ensures zero latency and absolute privacy (no data leaves your device). It is particularly effective for preprocessing images for OCR (via thresholding/binarization), simulating legacy display hardware (dithering/quantization), or developing custom edge-detection kernels for machine learning pipelines.

Key capabilities include a Linear Algebra Engine for color space rotation (simulating Protanopia or Technicolor), a Spatial Domain Processor for kernel convolution (Sobel, Laplacian, Gaussian), and a real-time Statistical Analyzer (Histogram). Precision is paramount; all internal calculations use floating-point arithmetic before final clamping to the [0, 255] integer range.

Formulas

The core transformation engine relies on two primary mathematical concepts: Matrix Convolution for spatial filtering and Affine Transformations for color correction.

1. Discrete Spatial Convolution
For an input image I and a kernel K of dimensions w×h, the output pixel value O(x,y) is the weighted sum of the neighborhood:

O(x, y) = h/2∑j=-h/2 w/2∑i=-w/2 I(x − i, y − j) ⋅ K(i, j)

2. Color Matrix Transformation
Color grading uses a 4×5 matrix multiplication (including an alpha channel and bias vector) applied to the color vector C = [R, G, B, A]^T:

R′G′B′A′ = rrrgrbragrgggbgabrbgbbba0001 RGBA + Offset_ROffset_GOffset_B0

Reference Data

Algorithm Family	Operation Type	Kernel / Matrix Structure	Engineering Use Case
Gaussian Smoothing	Spatial Convolution	121242121 × 116	Signal noise reduction, pre-processing for edge detection.
Sobel Operator	Gradient Magnitude	-101-202-101 (Gx)	Detecting vertical/horizontal edges in computer vision.
Laplacian	Second Derivative	0-10-14-10-10	Isotropic edge detection, zero-crossing identification.
Luminance (Rec. 709)	Linear Transformation	Y = 0.2126R + 0.7152G + 0.0722B	Standard HD Grayscale conversion matching human perception.
Floyd-Steinberg	Error Diffusion	Error Distribution: 716, 316, 516, 116	Converting continuous tone to binary for legacy printing.
Sepia Tone	Matrix Multiplication	.393.769.189.349.686.168.272.534.131	Archival simulation, warming color temperature.
Bayer Dither	Ordered Dithering	0821012414631119157135 × 116	Fast dithering for LCD displays and retro-gaming graphics.

Frequently Asked Questions

While WebGL is faster for rendering, CPU-based processing using `Uint8ClampedArray` allows for pixel-perfect accuracy required for scientific analysis, OCR preprocessing, and specific dithering algorithms (like Floyd-Steinberg) which are sequential and difficult to parallelize on a GPU. It also ensures the tool runs on legacy devices without WebGL support.

Ordered Dithering (e.g., Bayer) uses a fixed threshold map (a matrix) to decide if a pixel should be black or white. It creates a structured, cross-hatch pattern and is very fast. Error Diffusion (e.g., Floyd-Steinberg) calculates the quantization error for a pixel and pushes that error to neighboring pixels. It creates a more organic, noisy look but is computationally more expensive.

The Sobel operator calculates the gradient (rate of change) of image intensity. It uses two 3x3 kernels: one for horizontal changes (G_x) and one for vertical changes (G_y). The final edge magnitude is calculated as √G_x² + G_y². Areas with high gradient magnitude correspond to edges.

This tool provides standard algorithms used in these fields (e.g., CLAHE alternatives, high-pass filtering). However, it is a general-purpose tool. For medical diagnosis or forensic evidence, always use certified software that guarantees chain-of-custody and adherence to specific ISO standards.

The kernel size (e.g., 3x3, 5x5) determines the area of influence for each pixel. A larger kernel in a blur filter creates a stronger blur but requires significantly more processing power. The computational complexity grows quadratically with kernel size.