<p>The <em>filter_size</em> parameter in the tf.image.ssim function determines the size of the Gaussian filter used to smooth the image before calculating the SSIM.</p>
<p><strong>Option 1: Adjust Filter Parameters</strong></p>
<p>When using this function with an image smaller than 11x11 pixels (the default filter size), adjust the <em>filter_size</em> parameter to a smaller value. For an 8x11 image, a filter size of 3 or 5 would be appropriate.</p>
<p>While you don't necessarily need to adjust the <em>filter_sigma</em> parameter, it's worth experimenting with slightly smaller or larger values than the default (1.5) to see how it affects the results. Generally, a smaller filter size might work better with a slightly smaller sigma to avoid over-smoothing.</p>
<p>The best approach is to try different combinations of <em>filter_size</em> and <em>filter_sigma</em> to find the optimal values that balance accuracy and detail preservation for your specific image.</p>
<p><strong>Option 2: Pad the Image</strong></p>
<ul>
<li>Padding to your image, bringing it to a size of at least 11x11 pixels.</li>
<li>Be aware that the added padding will be included in the SSIM calculation, potentially affecting the results.</li>
</ul>
<p><strong>Choosing the Best Option:</strong></p>
<ul>
<li><p><strong>Simplicity:</strong> If you prefer a straightforward approach and don't mind the potential impact of padding on the SSIM results, padding the image is a good choice.</p>
</li>
<li><p><strong>Control and Detail Preservation:</strong> If you want more control over the calculation and need to prioritize detail preservation in small images, adjusting the filter parameters might be a better option.</p>
</li>
</ul>

CC BY-SA 4.0
2024-06-13T10:14:01.273Z
2024-06-13T10:14:01.273Z
Kulin Patel
<p>Can anyone please help me understand how to use this SSIM function (<a href="https://www.tensorflow.org/api_docs/python/tf/image/ssim" rel="nofollow noreferrer">https://www.tensorflow.org/api_docs/python/tf/image/ssim</a>)? The <code>filter_size</code> parameter has a default value of 11, according to the documentation information, &quot;the image sizes must be at least 11x11 because of the filter size.&quot; If I then use an image of size 8x11, how should I adjust this, and if applicable, other function parameters? Thanks.</p>

<python><tensorflow><metric><ssim>
How to use the tf.image.SSIM function
Marco
# How to use the tf.image.SSIM function

(Asked by: username_0 on 2024-06-12)

Can anyone please help me understand how to use this SSIM function (<https://www.tensorflow.org/api_docs/python/tf/image/ssim>)? The `filter_size` parameter has a default value of 11, according to the documentation information, "the image sizes must be at least 11x11 because of the filter size." If I then use an image of size 8x11, how should I adjust this, and if applicable, other function parameters? Thanks.

**Tags:** python, tensorflow, metric, ssim


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## 1 answers

### Answer by username_1 on 2024-06-13. Score: 1 | Accepted answer

The *filter\_size* parameter in the tf.image.ssim function determines the size of the Gaussian filter used to smooth the image before calculating the SSIM.

**Option 1: Adjust Filter Parameters**

When using this function with an image smaller than 11x11 pixels (the default filter size), adjust the *filter\_size* parameter to a smaller value. For an 8x11 image, a filter size of 3 or 5 would be appropriate.

While you don't necessarily need to adjust the *filter\_sigma* parameter, it's worth experimenting with slightly smaller or larger values than the default (1.5) to see how it affects the results. Generally, a smaller filter size might work better with a slightly smaller sigma to avoid over-smoothing.

The best approach is to try different combinations of *filter\_size* and *filter\_sigma* to find the optimal values that balance accuracy and detail preservation for your specific image.

**Option 2: Pad the Image**

* Padding to your image, bringing it to a size of at least 11x11 pixels.
* Be aware that the added padding will be included in the SSIM calculation, potentially affecting the results.

**Choosing the Best Option:**

* **Simplicity:** If you prefer a straightforward approach and don't mind the potential impact of padding on the SSIM results, padding the image is a good choice.
* **Control and Detail Preservation:** If you want more control over the calculation and need to prioritize detail preservation in small images, adjusting the filter parameters might be a better option.


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