Optimizing Images for LinkedIn: A Case Study
When deploying web applications, optimizing assets for social media sharing is crucial. The landing project, which focuses on creating engaging landing pages, recently tackled a common problem: image quality degradation on LinkedIn.
The Problem: LinkedIn's Recompression
LinkedIn converts uploaded images to JPEG, often resulting in visible pixelation, especially around sharp text and graphical elements. This is due to LinkedIn's recompression process, which can introduce artifacts and reduce the overall visual quality of the image. For landing pages and marketing materials, this can negatively impact the user's first impression.
The Solution: Pre-compression and Resolution Doubling
To mitigate this, the team implemented a strategy that involves rendering the LinkedIn banner at twice the intended resolution and pre-compressing it as a high-quality JPEG. This approach provides LinkedIn with more source pixels and allows for a gentler recompression pass. The key steps are:
- Rendering at 2x Resolution: The image is rendered at 2160x2700 pixels instead of the standard resolution.
- Pre-compression as JPEG: The image is then pre-compressed as a JPEG with a quality setting of 95.
The following example demonstrates how you might implement this using a hypothetical image processing library:
from PIL import Image
def optimize_for_linkedin(input_path, output_path, quality=95, scale_factor=2):
img = Image.open(input_path)
width, height = img.size
img = img.resize((int(width * scale_factor), int(height * scale_factor)), Image.Resampling.LANCZOS)
img.save(output_path, "JPEG", quality=quality)
# Example usage
optimize_for_linkedin("banner.png", "banner_optimized.jpg")
This code snippet first opens the input image using the Pillow library. It then doubles the resolution using Lanczos resampling to maintain quality during upscaling. Finally, it saves the image as a JPEG with a quality of 95.
The Result: Cleaner Images on LinkedIn
By implementing this strategy, the team observed a significant improvement in image quality on LinkedIn. The pre-compressed, high-resolution image survived LinkedIn's recompression with minimal artifacts, resulting in a cleaner, more professional-looking banner.
Key Takeaway
When sharing images on social media platforms like LinkedIn, be mindful of their recompression algorithms. Pre-compressing images at a higher resolution and quality can help preserve visual fidelity and ensure a better user experience. Experiment with different quality settings to find the optimal balance between file size and image quality for your specific use case.
