Image format mix
The image mix by format (JPEG, PNG, WebP, AVIF, SVG, GIF) - counts and bytes per format.
At a glance the headline numbers for Image format mix
The image mix by format (JPEG, PNG, WebP, AVIF, SVG, GIF) - counts and bytes per format.
5.4% of images are AVIF. 29.1% are still JPEG.
The image format mix mix who uses what, and how stable each group is
Image format mix. On the fleet: 29.1% jpg, 22.5% png, 19.1% webp.
Jpg leads by count (29.1%) and by bytes (53.8%). computed
Passing CLS per bucket every category and count level at once - color is the pass rate
Each row is a category, each column its own count bucket (few on the left, many on the right); the cell is the share of those sites passing CLS.
Gif swings the hardest: 90% of sites pass CLS with few, 83% with many. computed
Few vs many - does quantity cost CLS? the pass rate with few vs many of each category
Per category: the pass rate among pages with FEW of it (hollow ring) against pages with MANY (solid dot), worst trend first. Thin buckets are excluded from the endpoints.
More Gif costs the most: the CLS pass rate falls from 90% with few to 83% with many. computed
Why this matters for the Core Web Vitals, and where to start fixing it
Image bytes are usually the biggest slice of page weight, and the format multiplies all of them. AVIF and WebP deliver the same picture in far fewer bytes than JPEG. PNG belongs to screenshots and graphics, not photos. GIF is the most expensive way ever invented to ship a short video.
Nobody converts by hand at scale. An image CDN or build step negotiates the best format per browser from one source file. This mix mostly measures how many sites have that step in place.
Chrome field data from 189,915 sites, representing millions of real page loads. How we measured.
Live queries (2) — admin only
SELECT 1 AS ok FROM site_metric_bags LIMIT 0;WITH flat AS (
SELECT b.cat, b.n, b.size, s.crux."phone".cls AS cwv_val
FROM site_metric_bags b
JOIN sites s ON s.origin = b.origin
WHERE b.path = 'images.format'
AND b.cat IS NOT NULL
),
totals AS (
SELECT cat,
SUM(n) AS fleet_n,
SUM(size) AS fleet_size,
COUNT(*) FILTER (WHERE n IS NOT NULL) AS sample,
COUNT(*) FILTER (WHERE n > 0) AS with_any,
quantile_disc(n, 0.95) AS p95,
COUNT(*) AS n_rows
FROM flat
GROUP BY cat
HAVING COUNT(*) >= (SELECT COUNT(DISTINCT origin) FROM site_metric_bags WHERE path = 'images.format') * 0.005
),
widths AS (
SELECT *,
GREATEST(1, CAST(round(COALESCE(p95, 0)) AS INTEGER)) AS cap,
LEAST(GREATEST(1, CAST(round(COALESCE(p95, 0)) AS INTEGER)), 12 - 1) AS regular_buckets
FROM totals
),
widths2 AS (
SELECT *,
GREATEST(1, CAST(ceil(cap * 1.0 / regular_buckets) AS INTEGER)) AS width
FROM widths
),
binned AS (
SELECT f.cat, f.cwv_val, w.fleet_n, w.fleet_size, w.sample, w.with_any, w.width, w.regular_buckets,
CASE WHEN f.n IS NULL THEN NULL
ELSE LEAST(CAST(FLOOR(f.n * 1.0 / w.width) AS INTEGER), w.regular_buckets)
END AS bucket_idx
FROM flat f
JOIN widths2 w ON f.cat = w.cat
)
SELECT cat, fleet_n, fleet_size, sample, with_any, width, regular_buckets,
bucket_idx,
COUNT(*) AS n,
quantile_disc(cwv_val, 0.5) AS median,
COUNT(*) FILTER (WHERE cwv_val IS NOT NULL) AS total_cwv,
COUNT(*) FILTER (WHERE cwv_val IS NOT NULL AND cwv_val <= 0.1) AS good
FROM binned
WHERE bucket_idx IS NOT NULL
GROUP BY cat, fleet_n, fleet_size, sample, with_any, width, regular_buckets, bucket_idx
ORDER BY cat, bucket_idx;