dct-vision 0.1.0

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	Multiply coefficients by Gaussian envelope
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
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