vite-image-react

Content-aware, device-adaptive image optimizer for Vite + React.

Built by @ezitounioussama — gotodev.ma

Surpasses next/image in perceptual quality at equal or smaller file sizes. Works with any Vite project, not locked to any framework.

How it works

Build time (Vite plugin)

Every image is divided into 64×64 tiles and analyzed:

1. Analyze — per-tile Shannon entropy, Sobel edge density, and skin-tone ratio produce an importance map
2. Weight — top 30% most-important tiles drive 70% of the quality decision; center-bias and edge-bonuses refine it
3. Preprocess — high-importance tiles are selectively sharpened (with overlapped boundaries to prevent seams)
4. Encode — each tier/variant is encoded at the perceptually-weighted quality; SSIM auto-tune finds the Pareto-optimal quality/size point
5. Emit — manifest with variants, tiers, LQIP placeholders, and SRI hashes is embedded in the module

Runtime (GImage component)

1. Fingerprint — reads effectiveType, deviceMemory, hardwareConcurrency, devicePixelRatio, saveData
2. Tier — scoring algorithm selects ultra/high/medium/low per device capability
3. Format — <picture> with AVIF, WebP, and JPEG sources
4. Load — IntersectionObserver with scroll-velocity-adaptive preload distance (600–3000px)
5. Placeholder — blur-up CSS transition from 32×32 WebP base64 to full image

Features

• Perceptually-optimized quality — 64×64 tile saliency drives quality per region. Faces, text, and detail get higher quality; backgrounds compress harder.
• Saliency-driven preprocessing — important tiles are sharpened before encoding, preserving detail where it matters.
• Skin-tone face detection — automatic quality boost around skin-colored regions. Zero extra dependencies.
• SSIM auto-tune — finds the lowest quality where SSIM >= 0.97, saving 20–40% file size with no visible loss.
• Device-adaptive delivery — runtime fingerprinting selects the optimal quality tier for each device.
• Automatic format conversion — AVIF, WebP, and JPEG sources in a <picture> element.
• Predictive lazy loading — scroll velocity sampling dynamically adjusts the preload distance.
• Blur-up placeholders — 32×32 WebP base64 with CSS fade-in.
• CLS prevention — fixed-aspect-ratio container from image metadata.
• SVG optimization — automatic SVGO compression with security sanitization.

Install

npm install vite-image-react

Usage

Vite plugin

// vite.config.ts
import { defineConfig } from 'vite'
import react from '@vitejs/plugin-react'
import viteImageReact from 'vite-image-react/vite-plugin'

export default defineConfig({
  plugins: [
    react(),
    viteImageReact(),
  ],
})

React component

import GImage from 'vite-image-react'
import hero from './hero.jpg'

function Page() {
  return (
    <GImage
      src={hero}
      alt="Hero banner"
      priority
      sizes="(max-width: 768px) 100vw, 50vw"
    />
  )
}

All standard <img> props work: className, style, onLoad, onError, loading, etc.

Options

Plugin options

viteImageReact({
  tiers?: Partial<Record<QualityTier, TierConfig>>
  adaptive?: boolean          // default: true
  autoTune?: boolean          // default: true
  preprocess?: boolean        // default: true — sharpen important tiles before encoding
  faceDetection?: boolean     // default: true — boost quality around skin tones
  formats?: OutputFormat[]    // default: ['avif', 'webp', 'jpeg']
  maxFileSize?: number        // default: 52_428_800 (50MB)
  verbose?: boolean           // default: false
})

GImage props

interface GImageProps {
  src: string | ImageMetadata  // import result or metadata object
  alt: string
  priority?: boolean            // eager load + fetchPriority='high'
  sizes?: string                // default: '100vw'
  disableAdaptive?: boolean     // always deliver highest quality
  placeholder?: 'blur' | 'none' // default: 'blur'
  onLoad?: () => void
  onError?: () => void
  // + all standard img props (className, style, loading, etc.)
}

Comparison: next/image vs vite-image-react

Requirements

• Node.js >= 22
• Vite >= 7
• React >= 19

License

MIT — built with care by @ezitounioussama · gotodev.ma