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Converts the image to BT.601 luma at a fixed analysis size (long side analysis_max_side, default 1024; smaller images are not upscaled) and computes the variance of the 4-neighbour Laplacian (Pech-Pacheco et al. 2000) and the Tenengrad energy (mean squared Sobel magnitude). Because noise also raises the Laplacian variance, the Immerkær (1996) noise sigma is estimated and 20·sigma² (the Laplacian response to white noise) is subtracted.

All frames are scaled to the same long side (the smallest long side, at most 768 px) so that measures are comparable. Sharpness is the mean noise-corrected Laplacian variance (variance − 20·σ², σ by Immerkær 1996) of the 4 sharpest of 4×4 tiles, so the subject counts more than a blurred background; it is scored relative to the sharpest frame.

Converts opaque pixels of a copy with at most 512 px on the long side to CIELAB (D65, IEC 61966-2-1). Near-neutral reference: mean shift in the a*b* plane starting at (0, 0) with neutral_radius (default 12; the first step uses radius 25 only when fewer than 1 % of the pixels lie within it), centre kept within C* 25, over pixels with L* 20–95; at least 1 % of the pixels must belong to it.

Parses the HTML (htmlparser2) and its CSS (postcss for <style> blocks, own parser for style attributes, css-what for selectors) and maps what it finds to 304 of the 306 detectable caniemail features: elements and attributes, input and button types, mailto and anchor links, comments and doctype, CSS properties and selected values (display:flex/grid/none, flex-direction:column, system-ui fonts), at-rules and media features (prefers-color-scheme and others), selector types and combinators…

Analyses all opaque pixels (alpha ≥ 128) of a copy with at most 1024 px on the long side. The histogram uses 8-bit BT.709 luma of the encoded values. A pixel counts as clipped in the highlights when a channel reaches highlight_level (default 250; all channels = blown to white) and as crushed when every channel is at or below shadow_level (default 5).

Finds strong edges (Sobel magnitude above the 90th percentile and at least 16) whose orientation lies within max_angle of horizontal on a copy with at most 800 px on the long side, votes them into an orientation-constrained Hough accumulator (0.1° steps), takes the strongest line and refines it with a weighted total-least-squares fit of the supporting edge pixels. A horizon is accepted when the supporting pixels span at least a quarter of the width.

Lays out every string with fontkit (advance widths and GPOS/kern kerning) in Liberation Sans Regular or Bold (OFL, metric-compatible with Arial) or your own font, adds letter spacing per character, wraps at spaces like CSS white-space: normal with overflow-wrap: normal (a word wider than the container overflows) and compares with max_width and max_lines.

Blockiness: mean absolute luma difference across columns/rows at each of the 8 grid offsets divided by the mean elsewhere (1.0 = no blocking); analysed at native resolution on a central crop of at most 2048×2048 aligned to the 8-pixel grid, in stored orientation. A maximum at an offset other than (0,0) points to an earlier compression before a crop or shift (blocking-artifact grid, Li/Yuan/Yu 2009).

Uses the fast noise variance estimator of Immerkær (1996): the image is convolved with the 3×3 mask [1 −2 1; −2 4 −2; 1 −2 1], which cancels linear structure, and sigma = sqrt(π/2) · Σ|response| / (6·(W−2)(H−2)). Computed for BT.601 luma, the R, G and B channels and the JPEG Cb/Cr chroma planes. Because edges inflate the estimate, a second luma value excludes the 10 % of pixels with the strongest Sobel gradient (Tai & Yang 2008), and a tile median over up to 8×8 tiles is given.

Implements NIQE (Mittal, Soundararajan, Bovik, IEEE Signal Processing Letters 2013) following the LIVE reference code: grey image (rgb2gray weights, 8-bit), MSCN coefficients with a 7×7 Gaussian window (σ = 7/6), 96×96 patches at two scales (second scale by bicubic anti-aliased halving), 18 asymmetric generalised Gaussian features per patch and scale, a multivariate Gaussian fit and the Mahalanobis-type distance to the pristine model of the LIVE NIQE Software Release (© 2011 The University of…

Reads the image header (dimensions after EXIF orientation, colour space, ICC profile description, bit depth, stored density, alpha and actual transparency) and relates it to the print size plus bleed on each side. Paper sizes: ISO 216 A0–A7, B4, B5; ANSI letter, legal, tabloid; ISO/IEC 7810 ID-1 card; photo 4×6, 5×7, 8×10, 11×14 inch; or width_mm/height_mm. The paper is turned to match the image orientation.

Computes frequency-tuned saliency (Achanta et al., CVPR 2009): distance in CIELAB (D65) between each pixel of a 5×5 binomially smoothed copy and the image mean colour, on a copy with at most 400 px on the long side. For each ratio the largest crop of that ratio (full height or full width) is slid along the free axis; the position keeping the most saliency wins, ties go to the position nearest the saliency centroid. Protected regions (subject boxes you know, e.g.