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YUV vs LCH: What's the Difference and When to Use Each?
- Authors
- Name
- Satvik
- @toolschimp
Introduction
Color spaces in digital media are like specialized tools—each designed for a specific purpose and optimized for particular workflows. I've spent years working with both YUV and LCH color formats, and I've learned that the choice between them isn't just about technical specifications—it's about understanding how we transmit color versus how we perceive it. In this blog, I'll break down the origins, definitions, and practical uses of YUV and LCH, so you can confidently select the best format for your next project.
YUV and LCH represent two fundamentally different approaches to color representation. YUV (Y for luminance, U and V for chrominance) is designed around efficient video transmission and compression, while LCH (Lightness, Chroma, Hue) is designed around human perceptual uniformity. If you've ever wondered why video compression works so well, or why some color adjustments feel more intuitive than others, you're in the right place. Let's dive in and explore these essential color formats together.
YUV vs LCH: What's the Difference and When to Use Each?
What is YUV?
YUV stands for Y (luminance), U (blue chrominance), and V (red chrominance). It's a color space that separates brightness from color information, designed for efficient video transmission and compression. Y represents brightness (0-255), while U and V represent color differences (-128 to 127). For example:
yuv(76, 84, 255)
is pure redyuv(149, 43, 21)
is pure greenyuv(29, 255, 107)
is pure blueyuv(255, 128, 128)
is whiteyuv(0, 128, 128)
is black
What is LCH?
LCH stands for Lightness, Chroma, and Hue. It's a perceptually uniform color space that represents colors in a way that matches human visual perception. L represents lightness (0-100), C represents chroma (saturation, 0-150+), and H represents hue (0-360 degrees). For example:
lch(50, 100, 0)
is a medium-brightness redlch(75, 50, 120)
is a light greenlch(25, 80, 240)
is a dark bluelch(100, 0, 0)
is whitelch(0, 0, 0)
is black
Algorithm behind YUV to LCH Conversion and LCH to YUV Conversion
YUV to LCH Conversion
To convert YUV to LCH, we first convert YUV to RGB, then RGB to XYZ, then XYZ to Lab, and finally Lab to LCH. The algorithm involves multiple coordinate system transformations to bridge the transmission and perceptual color spaces.
function yuvToLch(y, u, v) {
// Convert YUV to RGB (BT.601)
const uNorm = u - 128
const vNorm = v - 128
const r = y + 1.402 * vNorm
const g = y - 0.344 * uNorm - 0.714 * vNorm
const b = y + 1.772 * uNorm
// Normalize RGB values to 0-1
const rNorm = Math.max(0, Math.min(255, r)) / 255
const gNorm = Math.max(0, Math.min(255, g)) / 255
const bNorm = Math.max(0, Math.min(255, b)) / 255
// Apply gamma correction
const rGamma = rNorm > 0.04045 ? Math.pow((rNorm + 0.055) / 1.055, 2.4) : rNorm / 12.92
const gGamma = gNorm > 0.04045 ? Math.pow((gNorm + 0.055) / 1.055, 2.4) : gNorm / 12.92
const bGamma = bNorm > 0.04045 ? Math.pow((bNorm + 0.055) / 1.055, 2.4) : bNorm / 12.92
// Convert to XYZ
const x = 0.4124 * rGamma + 0.3576 * gGamma + 0.1805 * bGamma
const y = 0.2126 * rGamma + 0.7152 * gGamma + 0.0722 * bGamma
const z = 0.0193 * rGamma + 0.1192 * gGamma + 0.9505 * bGamma
// Convert XYZ to Lab
const fx = x > 0.008856 ? Math.pow(x / 0.95047, 1 / 3) : 7.787 * x + 16 / 116
const fy = y > 0.008856 ? Math.pow(y / 1.0, 1 / 3) : 7.787 * y + 16 / 116
const fz = z > 0.008856 ? Math.pow(z / 1.08883, 1 / 3) : 7.787 * z + 16 / 116
const l = 116 * fy - 16
const a = 500 * (fx - fy)
const b = 200 * (fy - fz)
// Convert Lab to LCH
const c = Math.sqrt(a * a + b * b)
const h = (Math.atan2(b, a) * 180) / Math.PI
const hNormalized = h < 0 ? h + 360 : h
return {
l: Math.max(0, Math.min(100, l)),
c: Math.max(0, c),
h: hNormalized,
}
}
LCH to YUV Conversion
To convert LCH to YUV, we reverse the process: LCH to Lab, Lab to XYZ, XYZ to RGB, and finally RGB to YUV. The algorithm reconstructs the transmission color space from the perceptual color space.
function lchToYuv(l, c, h) {
// Convert LCH to Lab
const a = c * Math.cos((h * Math.PI) / 180)
const b = c * Math.sin((h * Math.PI) / 180)
// Convert Lab to XYZ
const fy = (l + 16) / 116
const fx = a / 500 + fy
const fz = fy - b / 200
const x = 0.95047 * Math.pow(fx, 3)
const y = 1.0 * Math.pow(fy, 3)
const z = 1.08883 * Math.pow(fz, 3)
// Convert XYZ to RGB
const r = 3.2406 * x - 1.5372 * y - 0.4986 * z
const g = -0.9689 * x + 1.8758 * y + 0.0415 * z
const b = 0.0557 * x - 0.204 * y + 1.057 * z
// Apply gamma correction and clamp
const rGamma = r > 0.0031308 ? 1.055 * Math.pow(r, 1 / 2.4) - 0.055 : 12.92 * r
const gGamma = g > 0.0031308 ? 1.055 * Math.pow(g, 1 / 2.4) - 0.055 : 12.92 * g
const bGamma = b > 0.0031308 ? 1.055 * Math.pow(b, 1 / 2.4) - 0.055 : 12.92 * b
const rNorm = Math.max(0, Math.min(1, rGamma))
const gNorm = Math.max(0, Math.min(1, gGamma))
const bNorm = Math.max(0, Math.min(1, bGamma))
// Convert RGB to YUV (BT.601)
const yLum = 0.299 * rNorm + 0.587 * gNorm + 0.114 * bNorm
const u = -0.169 * rNorm - 0.331 * gNorm + 0.5 * bNorm + 0.5
const v = 0.5 * rNorm - 0.419 * gNorm - 0.081 * bNorm + 0.5
return {
y: Math.max(0, Math.min(255, Math.round(yLum * 255))),
u: Math.max(0, Math.min(255, Math.round(u * 255))),
v: Math.max(0, Math.min(255, Math.round(v * 255))),
}
}
YUV vs LCH: What's the Difference?
When to Choose YUV?
- You're working with video compression and transmission
- You need efficient storage and bandwidth usage
- You're processing broadcast television signals
- You want to separate brightness from color information
- You're working with legacy video systems
When to Choose LCH?
- You're working with design systems and color palettes
- You need perceptually uniform color adjustments
- You're creating accessible color combinations
- You want intuitive color manipulation
- You're working with modern CSS and design tools
Understanding the Fundamental Differences
Feature | YUV (Transmission) | LCH (Perceptual) |
---|---|---|
Format | yuv(76, 84, 255) | lch(50, 100, 0) |
Color Space | Luminance + Chrominance | Perceptually uniform |
Human Perception | Not optimized | Optimized |
Compression Efficiency | High | Lower |
Use Case | Video, broadcasting | Design, accessibility |
File Size | Smaller | Larger |
Color and Range Limitations
- YUV is optimized for video compression and transmission
- LCH is designed for perceptual uniformity and accessibility
- YUV separates brightness from color for efficiency
- LCH has better color gamut representation
- Both can represent the same colors but with different approaches
Practical Examples
Examples of YUV to LCH Conversion
yuv(76, 84, 255)
→lch(53, 104, 0)
(red)yuv(149, 43, 21)
→lch(88, 119, 142)
(green)yuv(29, 255, 107)
→lch(32, 133, 306)
(blue)yuv(255, 128, 128)
→lch(100, 0, 0)
(white)yuv(0, 128, 128)
→lch(0, 0, 0)
(black)
Examples of LCH to YUV Conversion
lch(50, 100, 0)
→yuv(76, 84, 255)
(red)lch(75, 50, 120)
→yuv(149, 43, 21)
(green)lch(25, 80, 240)
→yuv(29, 255, 107)
(blue)lch(100, 0, 0)
→yuv(255, 128, 128)
(white)lch(0, 0, 0)
→yuv(0, 128, 128)
(black)
Common Conversion Challenges
- Complex multi-step conversion process between formats
- Precision loss during coordinate system transformations
- Different color gamut representations
- Performance considerations for real-time conversion
- Compatibility issues with different color standards
Best Practices for Conversion
- Use ToolsChimp YUV to LCH Converter for instant, accurate results
- Use ToolsChimp LCH to YUV Converter for reverse conversion
- Use YUV for video compression and transmission applications
- Use LCH for design and accessibility applications
- Consider the specific color standards for your use case
- See also: RGB vs YUV: What's the Difference and When to Use Each?
Features of YUV and LCH
YUV Features
- Efficient compression and transmission for video
- Separation of brightness and color information
- Standard for broadcast and video systems
- Smaller file sizes and bandwidth requirements
- Optimized for human visual perception in video
LCH Features
- Perceptually uniform color space for intuitive design
- Better color gamut representation and accessibility
- Modern CSS support and design tool compatibility
- Intuitive lightness, chroma, and hue controls
- Excellent for creating accessible color combinations
Use-cases of YUV and LCH
YUV Use-cases
- Video compression and streaming applications
- Broadcast television and cable systems
- Video conferencing and communication
- Digital video recording and storage
- Legacy video equipment and systems
LCH Use-cases
- Modern web design and CSS color manipulation
- Design systems and color palette creation
- Accessibility-focused color combinations
- Perceptually uniform color adjustments
- Creative design work with intuitive color control
Conclusion
In my experience, understanding YUV vs LCH: What's the Difference and When to Use Each? is crucial for anyone working with video processing or modern design systems. My recommendation? Use YUV when you're dealing with video compression, broadcasting, or need efficient transmission—it's optimized for video and saves bandwidth. Use LCH when you're working with design, accessibility, or need perceptually uniform color control—it's intuitive, modern, and perfect for creating accessible color combinations. The best approach is to understand both, use the right tool for the job, and always have reliable conversion tools at your fingertips. With these best practices, you'll be able to work with colors more effectively than ever before.
Frequently Asked Questions
Q: Which format is better for video processing?
A: YUV is better for video processing due to its efficient compression and transmission capabilities.
Q: Can I use YUV and LCH in the same project?
A: Yes, you can convert between them, but each is optimized for different use cases.
Q: Is one format more accessible than the other?
A: LCH is more accessible due to its perceptual uniformity and better color gamut representation.
Q: Which format should I use for web design?
A: Use LCH for web design as it's perceptually uniform and has modern CSS support.
Q: Why is YUV considered more efficient for video?
A: YUV is more efficient because it separates brightness from color information, allowing for better compression algorithms.
Q: Where can I learn more about color formats?
A: Check out RGB vs YUV: What's the Difference and When to Use Each? and explore more color tools on ToolsChimp.