dct-vision 0.2.0

Frequency-domain native image processing -- skip the pixel decode.

DCT-Vision

Frequency-domain native image processing. Operates directly on JPEG DCT coefficients, skipping the pixel decode step entirely.

Why?

JPEG images are already stored as DCT coefficients. Decoding to pixels just to blur/sharpen/adjust is wasteful. DCT-Vision works directly on the coefficients -- many operations become simple multiplications instead of expensive convolutions.

Performance

Benchmarked on 1024x1024 JPEG (operation time only, image already loaded):

Operation	DCT-Vision	Pillow	OpenCV	vs Pillow	vs OpenCV
Blur	2.0ms	21.1ms	1.2ms	10.5x	0.6x
Sharpen	1.9ms	19.3ms	2.9ms	10.1x	1.5x
Brightness	0.2ms	5.3ms	5.6ms	26.5x	28.0x
Contrast	0.6ms	14.5ms	15.4ms	24.2x	25.7x
Noise	13.6ms	58.4ms	53.5ms	4.3x	3.9x
Edge detect	0.8ms	11.6ms	0.9ms	14.5x	1.1x

Full pipeline (load + flip + brightness + noise + save, 1024x1024):

• DCT-Vision: 83ms | Pillow: 117ms | OpenCV: 107ms

Install

pip install dct-vision

Quick start

Python API

from dct_vision.core.dct_image import DCTImage
from dct_vision.ops.blur import blur
from dct_vision.ops.color import adjust_brightness

# Load JPEG (extracts DCT coefficients directly, no pixel decode)
img = DCTImage.from_file("photo.jpg")

# Process in frequency domain
img = blur(img, sigma=2.0)
img = adjust_brightness(img, offset=20)

# Save (writes coefficients directly, no pixel encode)
img.save("output.jpg")

CLI

dv blur photo.jpg -o blurred.jpg --sigma 2.0
dv sharpen photo.jpg -o sharp.jpg --amount 1.5
dv brightness photo.jpg -o bright.jpg --offset 30
dv contrast photo.jpg -o contrast.jpg --factor 1.5
dv downscale photo.jpg -o small.jpg --factor 2
dv edges photo.jpg -o edges.jpg --method laplacian
dv info photo.jpg --json
dv quality photo.jpg
dv convert input.png -o output.jpg --quality 85
dv augment photo.jpg -o aug.jpg --flip horizontal --noise 3.0 --seed 42

ML Augmentation Pipeline

from dct_vision.core.dct_image import DCTImage
from dct_vision.augment.flip import horizontal_flip
from dct_vision.augment.jitter import brightness_jitter
from dct_vision.augment.noise import gaussian_noise

img = DCTImage.from_file("train/img_001.jpg")
img = horizontal_flip(img)
img = brightness_jitter(img, max_offset=20, seed=42)
img = gaussian_noise(img, sigma=2.0, seed=42)
img.save("augmented/img_001.jpg")

Operations

Operation	Type	How it works
Gaussian blur	Tier 1/2	Multiply coefficients by Gaussian envelope (cross-block for sigma > 2)
Sharpening	Tier 1	Boost high-frequency coefficients
Brightness	Tier 1	Offset DC coefficient (block mean)
Contrast	Tier 1	Scale AC coefficients (deviation from mean)
Downscale 2x	Tier 1	Merge 2x2 block groups via transform matrix
Edge detection	Tier 2	Laplacian or gradient in frequency domain
Sobel edge detection	Tier 1	Directional frequency gradient weights
Scharr edge detection	Tier 1	Weighted directional gradient (more accurate)
Box blur	Tier 1	Sinc-like frequency envelope
Emboss	Tier 1	Directional frequency emphasis
Band-pass filter	Tier 1	Keep mid-frequency coefficients (no OpenCV equivalent)
Unsharp mask	Tier 1	1 + amount * (1 - Gaussian envelope)
Color temperature	Tier 1	Shift Cb/Cr DC coefficients
Saturation	Tier 1	Scale Cb/Cr coefficients
Wiener denoising	Tier 1	Optimal frequency-domain noise filter
JPEG deblocking	Tier 1	Attenuate high-freq quantization artifacts
Perceptual hash (pHash)	Tier 1	Hash from DC coefficients (native DCT advantage)
Blur detection	Analysis	High-freq to total energy ratio
Noise estimation	Analysis	Std of highest-frequency coefficients
Texture complexity	Analysis	Nonzero AC coefficient ratio
Image similarity	Analysis	Normalized cross-correlation of coefficients
Vignette	Photo	Distance-weighted block attenuation
Sepia / tint	Photo	Set Cb/Cr to fixed warm values
Grayscale conversion	Photo	Drop Cb/Cr channels (zero cost)
Posterize	Photo	Aggressive coefficient requantization
Solarize	Photo	Invert coefficients above threshold
Requantize (change JPEG quality)	Compression	Apply new quant table without decode
Coefficient pruning	Compression	Zero small AC coefficients to reduce file size
Quality estimation	Tier 1	Reverse-engineer quality from quant tables
Horizontal/vertical flip	Augment	Negate odd-indexed frequency coefficients
Block crop	Augment	Slice coefficient array directly
Brightness/contrast jitter	Augment	Random DC/AC perturbation
Gaussian noise	Augment	Add noise to AC coefficients

Documentation

• Installation guide
• CLI reference
• Python API
• Architecture

Requirements

• Python 3.10+
• libjpeg-turbo (for native DCT extraction; falls back to Pillow if unavailable)

License

MIT