Optimizing Image Formats for Computer Vision and Image Processing: PNG, JPG, and WEBP in OpenCV

Original Image from Valeriia, from Pexels.com | result image by the author

Choosing the right image format can impact performance and quality in computer vision and image processing applications. Whether you’re pre-processing data for training deep learning models, running inference on real-time systems, or working with large datasets, understanding the strengths and weaknesses of PNG, JPG, and WEBP can help you make informed choices.

Let’s dive into the unique characteristics of each format in the context of image processing, along with practical code examples to load and save these formats using OpenCV in Python.

Table of Content

1. PNG (Portable Network Graphics)
2. JPG/JPEG (Joint Photographic Experts Group)
3. WEBP (Web Picture Format)

1. PNG (Portable Network Graphics)

Strengths:

PNG supports lossless compression, retaining all image details and supporting transparency. PNG is often ideal for image processing tasks where precise pixel values matter (e.g., segmentation masks or scientific image analysis).

Weaknesses:
PNG files are typically larger, which can slow down processing pipelines, especially when handling large datasets. This can affect performance if storage or bandwidth is a limitation, making PNG less ideal for real-time or resource-constrained applications.

Usage in OpenCV:

import cv2

# Reading a PNG image
image = cv2.imread("example.png", cv2.IMREAD_UNCHANGED)  # To retain transparency if present

# Saving as PNG with maximum quality (lossless compression)
cv2.imwrite("output.png", image, [cv2.IMWRITE_PNG_COMPRESSION, 0])  # 0 is lossless, 9 is max compression

2. JPG / JPEG(Joint Photographic Experts Group)

Strengths:
JPG is widely used for photographs and natural images, with efficient lossy compression. It’s perfect for reducing file sizes in large image datasets or when speed is crucial. In computer vision, JPG is often used for datasets where pixel accuracy is less critical, like object detection or classification tasks.

Weaknesses:
JPG’s lossy nature results in some data loss, especially after multiple saves, which can degrade image quality over time. It also does not support transparency, limiting its use for certain applications.

Usage in OpenCV:

import cv2

# Reading a JPG image
image = cv2.imread("example.jpg")

# Saving as JPG with quality control (lossy compression)
cv2.imwrite("output.jpg", image, [cv2.IMWRITE_JPEG_QUALITY, 90])  # Quality range: 0 to 100, 100 is best quality

3. WEBP (Web Picture Format)

From Google’s official documentation

You can read the full document here https://developers.google.com/speed/webp/docs/compression

Strengths:
WEBP offers both lossy and lossless compression, making it a versatile choice. It combines the transparency of PNG and the compression efficiency of JPG, which is advantageous for computer vision applications requiring high performance and storage efficiency. For machine learning, using WEBP can save storage space and speed up dataset loading, especially with large datasets.

Weaknesses:
Despite its efficiency, WEBP is still not universally supported across all platforms or legacy software. However, for image processing workflows with modern libraries, WEBP is an increasingly robust choice.

I also recommend taking a look at a study made by Google comparing Webp and JPG. https://developers.google.com/speed/webp/docs/webp_study

Usage in OpenCV:

# Reading a WEBP image
image = cv2.imread("example.webp", cv2.IMREAD_UNCHANGED)

# Saving as WEBP with compression control
# Lossless compression (quality=100) vs. lossy compression (quality < 100)
cv2.imwrite("output_lossy.webp", image, [cv2.IMWRITE_WEBP_QUALITY, 75])    # Lossy with quality at 75
cv2.imwrite("output_lossless.webp", image, [cv2.IMWRITE_WEBP_QUALITY, 100]) # 100 enables lossless

Experiments

Result by the author | Original Image from Pexels.com, by Valeriia Harbuz

Result by the author | Original Image from Pexels.com, by Impact Dog Crates

Result by the author | Original Image from Pexels.com, by Juliana Levin Schottz

Comparison Summary

• PNG: Excellent for lossless compression and transparency; best for pixel-accurate applications but can be storage-heavy.
• JPG: This format is ideal for natural images where some loss in quality is acceptable. It is great for large datasets but unsuitable for images needing transparency or exact pixel preservation.
• WEBP: is versatile, with both lossy and lossless options. It is effective for reducing storage usage while maintaining high quality, ideal for computer vision applications requiring fast access and moderate compression.

Choosing the right image format and settings is essential in maximizing the efficiency and performance of computer vision and image processing workflows. Whether you’re training models, analyzing data, or deploying applications, understanding these differences allows you to optimize for quality, speed, and storage — leading to more robust and efficient systems.