Interpolation is one of the most important concepts in computer vision and image processing. If you work with OpenCV, you use interpolation every time you resize, rotate, or transform an image - even if you don't realize it.

This guide covers: - What interpolation means

• Why OpenCV uses interpolation
• Types of interpolation
• Practical code examples

What Is Interpolation?

Interpolation is the process of estimating pixel values between known pixels.

When an image is transformed (resized, rotated, zoomed), some pixel positions in the output have no direct mapping from the input. Interpolation helps estimate those missing pixel values.

In simple terms: Interpolation fills the gaps when an image is resized or transformed.

Why Is Interpolation Needed in OpenCV?

OpenCV uses interpolation in:

• Image resizing
• Image rotation
• Affine transform
• Perspective transform
• Image warping
• Zooming in/out

Without interpolation, images would appear blocky, torn, or low-quality.

Types of Interpolation in OpenCV (With Examples)

Below are the main interpolation methods with sample code and explanations.

1. INTER_NEAREST - Nearest Neighbor

Picks the closest pixel value.

• Fastest
• Pixelated/blocky output
• Best for segmentation masks & labels

import cv2
 
img = cv2.imread("image.jpg")
nearest = cv2.resize(img, None, fx=3, fy=3, interpolation=cv2.INTER_NEAREST)
cv2.imwrite("nearest_output.jpg", nearest)

2. INTER_LINEAR - Bilinear Interpolation (Default)

Interpolates using weighted average of 4 neighboring pixels.

• Good balance of quality and speed
• Default in most OpenCV functions

linear = cv2.resize(img, None, fx=3, fy=3, interpolation=cv2.INTER_LINEAR)
cv2.imwrite("linear_output.jpg", linear)

3. INTER_CUBIC - Bicubic Interpolation

Uses 16 neighboring pixels for smoother results.

• High-quality upscaling
• Slower than linear

cubic = cv2.resize(img, None, fx=3, fy=3, interpolation=cv2.INTER_CUBIC)
cv2.imwrite("cubic_output.jpg", cubic)

4. INTER_AREA - Best for Downscaling

Averages pixel values over an area for clean shrinking.

• Sharp output when reducing image size
• Avoids aliasing

area = cv2.resize(img, None, fx=0.3, fy=0.3, interpolation=cv2.INTER_AREA)
cv2.imwrite("area_output.jpg", area)

5. INTER_LANCZOS4 - High-Quality Resampling

Uses a Lanczos kernel with 8×8 neighborhood.

• Highest quality
• Slowest
• Best for photography, editing, printing

lanczos = cv2.resize(img, None, fx=3, fy=3, interpolation=cv2.INTER_LANCZOS4)
cv2.imwrite("lanczos_output.jpg", lanczos)

Full Example: Comparing All Methods

import cv2
 
img = cv2.imread("input.jpg")
 
methods = {
    "nearest": cv2.INTER_NEAREST,
    "linear": cv2.INTER_LINEAR,
    "cubic": cv2.INTER_CUBIC,
    "area": cv2.INTER_AREA,
    "lanczos": cv2.INTER_LANCZOS4
}
 
for name, method in methods.items():
    out = cv2.resize(img, None, fx=2, fy=2, interpolation=method)
    cv2.imwrite(f"output_{name}.jpg", out)

Summary Table

Interpolation is essential for clean image resizing, smooth rotations, and high-quality transformations. OpenCV provides multiple interpolation options so you can choose the best one based on your task.