Choosing Between Lanczos-3 Separable and Lanczos-3 Non-Separable Image Resizing Methods
A little on the technical side this week, but I thought this post might be helpful for others in a similar situation. Recently I was working on some images in Affinity Photo and some of which needed a slight resize. I was looking through the list of resize methods trying to decide what to use. Most are pretty standard and I’m familiar enough with them. Except Lanczos 3 and its two types – separable and non-separable. I feel like I’ve seen them before somewhere but knew nothing about them.
According to my research, they’re part of the family of Lanczos resampling methods, which are designed to improve the quality of resized images compared to simpler algorithms like bilinear or nearest-neighbour interpolation.
There’s lots of technical data on Lanczos in general, but not much that explains the difference between between the two Lanczos 3 types. Should you go with the separable or non-separable approach? I’ve got you covered! Let’s explore the key differences and help you make an informed choice..
Understanding Lanczos-3 Resampling
Before diving into the differences, let’s briefly recap what Lanczos-3 resampling is all about. Lanczos-3 is a resampling or interpolation algorithm used to resize images while preserving their quality. It’s a more advanced method compared to simpler algorithms like nearest-neighbor or bilinear interpolation.
Now, let’s break down the separation.
Lanczos-3 Separable: Speed and Efficiency
Lanczos-3 Separable takes a pragmatic approach. It divides the resampling process into two separate steps: first horizontally and then vertically (or vice versa). Imagine it as a two-pass system where each pass focuses on one dimension. This separation brings notable advantages:
Efficiency: The separable method is computationally more efficient and requires less memory. It processes the image row by row and then column by column, which reduces the overall complexity.
Speed: Due to its efficiency, Lanczos-3 Separable is a preferred choice for real-time applications or situations where computational resources are limited.
However, there’s a trade-off to consider.
Lanczos-3 Non-Separable: Superior Image Quality
On the other side of the spectrum, we have Lanczos-3 Non-Separable. This approach doesn’t split the resampling process into separate horizontal and vertical passes. Instead, it considers both dimensions simultaneously. The result? Slightly better image quality.
Image Quality: Lanczos-3 Non-Separable delivers a marginal boost in image quality because it takes a holistic approach, accounting for both horizontal and vertical contributions in a single pass.
Resource Intensive: While it excels in image quality, this method demands more computational resources and memory. So, it may not be ideal for applications where speed and efficiency are top priorities.
Choosing the Right Lanczos-3 for Your Needs
The choice between Lanczos-3 Separable and Lanczos-3 Non-Separable comes down to your specific requirements and constraints:
Lanczos-3 Separable: Opt for this when you need a balance between image quality and computational efficiency. It’s excellent for most applications and especially suited for real-time scenarios or resource-limited environments.
Lanczos-3 Non-Separable: If image quality is your utmost concern, and you’re willing to invest more processing time and memory for that extra finesse, then this is your choice.
In summary, Lanczos-3 resampling offers a powerful tool for maintaining image quality during resizing. By understanding the differences between its separable and non-separable variants, you can make an informed decision based on your project’s priorities. Whether you prioritize efficiency or the finest image quality, Lanczos-3 is a great choice!.
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