pic_scale 0.7.8

High-performance image resampling with a Pillow-compatible API

pic-scale

High-performance image resampling for Python — a drop-in replacement for PIL.Image.resize backed by a SIMD-optimized Rust engine.

Installation

pip install pic-scale

Pre-built wheels for Linux (x86-64, aarch64), macOS (Intel + Apple Silicon), and Windows (x86-64). No Rust toolchain needed.

Quick start

from PIL import Image
from pic_scale import resize, Resampling

img = Image.open("photo.jpg")
out = resize(img, (800, 600), Resampling.LANCZOS)
out.save("small.jpg")

That's the entire API change from Pillow — swap img.resize(...) for resize(img, ...).

Resampling filters

Filter	Resampling constant	Notes
Nearest neighbour	NEAREST	Fastest, blocky
Bilinear	BILINEAR	Fast, smooth
Bicubic	BICUBIC	Keys cubic a = −0.5
Lanczos / sinc	LANCZOS	Window 3, Pillow default for quality
Lanczos 2	LANCZOS2	Faster, slightly softer
Lanczos 4	LANCZOS4	Slower, very sharp
Box / area	BOX	Best for downscaling
Hamming	HAMMING
Mitchell-Netravali	MITCHELL	B=1/3 C=1/3, balanced
Catmull-Rom	CATMULL_ROM	Sharper than Mitchell
Gaussian	GAUSSIAN
Hann	HANN

Supported Pillow modes

Mode	Description
L	8-bit grayscale
LA	8-bit grayscale + alpha
RGB	8-bit colour
RGBA	8-bit colour + alpha
I;16	16-bit grayscale
F	32-bit float grayscale

Other Pillow modes (CMYK, YCbCr, P, …) must be converted first:

out = resize(img.convert("RGB"), (800, 600))

Alpha premultiplication

For LA and RGBA images, premultiply_alpha=True (the default) pre-multiplies RGB by alpha before resampling and un-multiplies afterward. This prevents dark fringing around transparent edges — the same technique Photoshop and modern compositors use.

# Correct — no dark halos around transparency
out = resize(img, (400, 400), Resampling.LANCZOS, premultiply_alpha=True)

# Disable if your pipeline handles premultiplication separately
out = resize(img, (400, 400), Resampling.LANCZOS, premultiply_alpha=False)

Multithreading

Pass workers to use multiple threads. 0 lets pic-scale choose automatically based on the image size and core count.

out = resize(img, (3840, 2160), Resampling.LANCZOS, workers=0)

Plan — reuse across many frames

When resizing a stream of images with the same source and target dimensions, create a Plan once and reuse it. Filter weights are computed once at construction; subsequent calls skip that work entirely.

from pic_scale import Plan, Resampling

plan = Plan(
    src_size=(1920, 1080),
    dst_size=(960,  540),
    resampling=Resampling.LANCZOS,
    mode="RGB",
)

for frame in video_frames:
    small = plan.resize(frame)
    encoder.write(small)

The plan validates that each incoming image has the correct mode and raises ValueError immediately if it doesn't, so mode mismatches are caught early.

API reference

resize(image, size, resampling, premultiply_alpha, workers)

Parameter	Type	Default	Description
image	PIL.Image.Image	—	Source image
size	(int, int)	—	Target (width, height)
resampling	Resampling	LANCZOS	Filter
premultiply_alpha	bool	True	Premultiply alpha for LA/RGBA
workers	int	1	Thread count; 0 = adaptive

Returns a new PIL.Image.Image in the same mode as the input.

Plan(src_size, dst_size, resampling, mode, premultiply_alpha, workers)

Parameter	Type	Default	Description
src_size	(int, int)	—	Source (width, height)
dst_size	(int, int)	—	Target (width, height)
resampling	Resampling	—	Filter
mode	str	—	Pillow mode string
premultiply_alpha	bool	True	Premultiply alpha
workers	int	1	Thread count; 0 = adaptive

Plan.resize(image) → PIL.Image.Image

Resize image using the pre-computed plan. The image's mode must match the mode the plan was created with.

Comparison with Pillow

# Pillow
out = img.resize((800, 600), Image.Resampling.LANCZOS)

# pic-scale — same result, faster
from pic_scale import resize, Resampling
out = resize(img, (800, 600), Resampling.LANCZOS)

pic-scale does not replace Pillow — it only replaces the resize step. All other Pillow operations (open, save, colour conversion, drawing, …) continue to use Pillow as normal.

License

BSD 3-Clause