Vector vs Raster

Common uses for raster graphics include

• Digital photography
   
• Web graphics
   
• Photo editing & manipulation
   
• Detailed illustrations & artwork

Some popular raster file formats are JPEG, PNG, GIF, & TIFF.

Vector Graphics

Vector graphics are digital images created using mathematical equations & geometric shapes like points, lines, & curves. Unlike raster graphics, vector graphics are resolution-independent, which means they can be scaled up or down without losing quality. 

When you zoom in on a vector graphic, the edges remain sharp & clear, no matter how much you magnify the image.

Vector graphics are useful for:

• Logos & brand graphics
   
• Illustrations & icons
   
• Typography & text-based designs
   
• Technical drawings & diagrams
   

Common vector file formats include AI (Adobe Illustrator), EPS (Encapsulated PostScript), & SVG (Scalable Vector Graphics).

The main advantage of vector graphics is their flexibility & scalability. They can be resized without losing quality, making them perfect for designs that need to be used across various media, from business cards to billboards.

Conversions

Vector to Raster

Converting a vector graphic to a raster image is a common process, especially when preparing designs for digital use. This conversion is called rasterization. When you rasterize a vector graphic, you essentially create a pixel-based version of the image at a specific resolution.

To convert a vector graphic to a raster image, follow these general steps:

1. Open the vector file in a graphics editor that supports both vector & raster formats (e.g., Adobe Illustrator or Inkscape).
    
2. Choose the desired resolution for the raster image. Keep in mind the intended use of the image (web, print, etc.) & select an appropriate resolution.
    
3. Export or save the file in a raster format, such as JPEG, PNG, or TIFF.
    

Here’s a basic example in Python, using the Pillow library to convert an SVG vector image to a PNG raster image:

from PIL import Image
import requests
from io import BytesIO
# Load SVG file from a URL
response = requests.get('http://example.com/image.svg')
svg_image = Image.open(BytesIO(response.content))
# Convert SVG to PNG
png_image = svg_image.convert('RGB')
png_image.save('converted_image.png')

Note : It's important to note that once a vector graphic is rasterized, it loses its resolution-independence & becomes a fixed-size raster image.

Raster to Vector

Converting a raster image to a vector graphic is a more complex process than converting vector to raster. This conversion is called vectorization or tracing. Vectorization involves analyzing the pixels in a raster image & creating vector shapes that closely match the original image.

There are two main methods for vectorizing a raster image:

1. Manual tracing: This involves using a graphics editor's pen or path tools to manually trace over the raster image, creating vector shapes as you go. This method gives you the most control over the final result but can be time-consuming, especially for complex images.
    
2. Automatic tracing: Many graphics editors, such as Adobe Illustrator & Inkscape, have built-in tools for automatically tracing raster images. These tools analyze the pixels in the image & generate vector shapes based on the colors & shapes they detect. While automatic tracing is faster than manual tracing, the results may not always be perfect & may require some manual adjustments.
    

To vectorize a raster image using automatic tracing in Adobe Illustrator, follow these steps:

1. Open the raster image in Adobe Illustrator.
    
2. Select the image & go to "Window" > "Image Trace" to open the Image Trace panel.
    
3. Choose a preset or adjust the settings to control the level of detail & colors in the traced image.
    
4. Click "Expand" to convert the traced image into editable vector shapes.
    

Example using Python’s opencv library to trace a simple raster shape into vector paths:

import cv2
import numpy as np
# Load a raster image
image = cv2.imread('input.jpg', 0)
# Threshold the image
_, threshold = cv2.threshold(image, 127, 255, cv2.THRESH_BINARY)
# Find contours from the threshold image
contours, _ = cv2.findContours(threshold, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
# Draw contours on a blank image
contour_image = np.zeros_like(image)
cv2.drawContours(contour_image, contours, -1, (255, 0, 0), 3)
# Save the result
cv2.imwrite('vectorized_output.jpg', contour_image)

Note : Keep in mind that not all raster images are suitable for vectorization. Images with complex textures, gradients, or photographic elements may not trace well & may be better suited to remaining in a raster format.

Differences between Vector and Raster Graphics

Frequently Asked Questions

Can vector graphics be used for photographs?

Vector graphics are not ideal for photographs due to their nature of defining images through shapes and not pixels, which are better for capturing the complex color variations and details in photos.

Why do raster images lose quality when enlarged?

Raster images are made up of a fixed number of pixels, so enlarging them causes pixelation—each pixel becomes more visible, making the image appear blurry or blocky.

Which graphic type is more versatile for web use?

Raster graphics are generally more versatile for web use, especially for displaying detailed images like photographs. However, for logos and icons, vector graphics are preferred due to their scalability.

Conclusion 

In this article, we've discussed the fundamentals of vector & raster graphics, including their definitions, key terms, differences, & the processes for converting between the two formats. Understanding the strengths & limitations of each format is crucial for creating, editing, & sharing digital images effectively. Choosing the right format for your project & knowing how to work with both vector & raster graphics is very crucial in this. We explained the difference with proper code examples to clear any doubt.

You can refer to our guided paths on the Coding Ninjas. You can check our course to learn more about DSA, DBMS, Competitive Programming, Python, Java, JavaScript, etc. Also, check out some of the Guided Paths on topics such as Data Structure andAlgorithms, Competitive Programming, Operating Systems, Computer Networks, DBMS, System Design, etc., as well as some Contests, Test Series, and Interview Experiences curated by top Industry.