Title: Optimize and Compress Images
Version: 0.4
Description: Optimize and compress images using 'Rust' libraries to reduce file sizes while maintaining image quality. Supports PNG palette reduction and dithering via the 'exoquant' crate before lossless PNG optimization via the 'oxipng' crate, and JPEG re-encoding via the 'mozjpeg' crate. The package provides functions to optimize individual image files or entire directories, with configurable compression levels. Use tinyimg() as a convenient entry point for mixed PNG/JPEG workflows.
License: MIT + file LICENSE
URL: https://github.com/yihui/tinyimg
BugReports: https://github.com/yihui/tinyimg/issues
SystemRequirements: Cargo (Rust's package manager), rustc
Encoding: UTF-8
RoxygenNote: 7.3.3
Suggests: testit
Config/rextendr/version: 0.3.1
NeedsCompilation: yes
Packaged: 2026-04-14 17:23:37 UTC; yihui
Author: Yihui Xie ORCID iD [aut, cre, cph] (URL: https://yihui.org), Authors of the dependency Rust crates [ctb, cph] (see AUTHORS file)
Maintainer: Yihui Xie <xie@yihui.name>
Repository: CRAN
Date/Publication: 2026-04-14 18:00:03 UTC

tinyimg: Optimize and Compress Images

Description

The tinyimg package provides tools for optimizing and compressing images using Rust libraries. Use tinyimg() for a convenient entry point that handles both PNG and JPEG files, tinypng() for PNG-only optimization, or tinyjpg() for JPEG-only optimization.

Author(s)

Maintainer: Yihui Xie xie@yihui.name (ORCID) (https://yihui.org) [copyright holder]

Other contributors:

See Also

Useful links:

Optimize PNG and JPEG images

Description

tinyimg() dispatches PNG files to tinypng() and JPEG files to tinyjpg(). tinypng() optimizes PNG files using optional lossy palette reduction and dithering (via the exoquant crate) before lossless compression (via the oxipng crate). tinyjpg() optimizes JPEG files by re-encoding with mozjpeg's optimized Huffman coding and quantization tables.

Usage

tinyimg(
  input,
  output = tiny_output,
  recursive = TRUE,
  verbose = TRUE,
  level = 2L,
  quality = 75,
  lossy = 0,
  ...
)

tinyjpg(
  input,
  output = tiny_output,
  quality = 75,
  recursive = TRUE,
  verbose = TRUE
)

tiny_output(input, lossy = 0, quality = 75)

tinypng(
  input,
  output = tiny_output,
  level = 2L,
  alpha = FALSE,
  preserve = TRUE,
  recursive = TRUE,
  verbose = TRUE,
  lossy = 0
)

Arguments

input

Path to an image file, a character vector of image file paths, or a directory. tinyimg() accepts .png, .apng, .jpg, and .jpeg files; tinypng() accepts .png and .apng; tinyjpg() accepts .jpg and .jpeg.

output

Path to the output file or directory, a function that maps input paths to output paths, or identity to optimize in place. Defaults to tiny_output(), which adds a suffix encoding the optimization parameters so that the original file is never overwritten by a lossy result.

recursive

When input is a directory, also search subdirectories.

verbose

Print file size change info for each file.

level

PNG optimization level (0–6). Higher values give better compression but take longer. Passed to tinypng() by tinyimg().

quality

JPEG quality level (0–100). Higher quality means larger files; lower quality means smaller files. Passed to tinyjpg() by tinyimg(). tiny_output() appends ⁠_q<value>⁠ when quality < 100.

lossy

Numeric threshold for per-color \Delta E_{76} in lossy PNG palette reduction. Values ⁠<= 0⁠ disable lossy optimization. See Details. Passed to tinypng() by tinyimg() via .... When ⁠> 0⁠, tiny_output() appends ⁠_l<value>⁠ to the output filename.

...

Additional arguments passed from tinyimg() to tinypng() (e.g., alpha, preserve).

alpha

Optimize transparent pixels in PNG files for better compression. This is technically lossy but visually lossless.

preserve

Preserve file permissions and timestamps when optimizing PNG files. Ignored when lossy > 0.

Details

tiny_output() generates output file paths by appending a suffix that encodes the optimization parameters, e.g., foo_l2.3.png for lossy PNG (lossy = 2.3) and foo_q70.jpg for JPEG at quality = 70. It is the default output for all three optimizers so that lossy or quality-reduced results are never silently written over the original files. Pass output = identity to optimize in place (lossless PNG optimization is always safe to do in place).

The lossy PNG algorithm uses color difference in the CIE 1976 L^*a^*b^* color space. For a candidate palette size n, the image is quantized with n colors using nearest-color mapping. Sampled pixels are then grouped by their original color, so each distinct color gets one equal vote regardless of how many pixels share it (preventing a large uniform background from masking errors in rarer content colors). The worst-case \Delta E_{76} within each group is recorded, and the 95th percentile of those per-color values is taken. Bisection on n (1–256) finds the smallest palette whose per-color p95 is ⁠<= lossy⁠.

\Delta E_{76} \approx 2.3 is the just noticeable difference (JND) threshold. Larger values allow more color difference and smaller palettes, with more loss of color fidelity.

Value

tinyimg(), tinypng(), and tinyjpg() invisibly return a character vector of output file paths. tiny_output() returns a character vector of output file paths (visibly).

References

https://en.wikipedia.org/wiki/Color_difference

Examples

# Create test images
tmp_png = tempfile(fileext = ".png")
png(tmp_png, width = 400, height = 400); plot(1:10); dev.off()

tmp_jpg = tempfile(fileext = ".jpg")
jpeg(tmp_jpg, width = 400, height = 400); plot(1:10); dev.off()

# Optimize both in one call (uses tiny_output() by default)
tinyimg(c(tmp_png, tmp_jpg))

# Optimize in place (lossless PNG is safe; use with care for JPEG)
tinypng(tmp_png, identity)
tinyjpg(tmp_jpg, identity)

# Lossy PNG: output gets a suffix automatically
tinypng(tmp_png, lossy = 2.3)

# JPEG at a specific quality
tinyjpg(tmp_jpg, quality = 60)

# See what output names tiny_output() would generate
tiny_output(c(tmp_png, tmp_jpg), lossy = 2.3, quality = 60)