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README.md

@@ -12,37 +12,59 @@ pinned: false
 
 # Metric Card for Peak Signal to Noise Ratio
 
-***Module Card Instructions:*** *Fill out the following subsections. Feel free to take a look at existing metric cards if you'd like examples.*
+
 
 ## Metric Description
-*Give a brief overview of this metric, including what task(s) it is usually used for, if any.*
+It is the ratio between the maximum possible power of a signal and the power of
+corrupting noise that affects the fidelity of its representation. This metric is
+commonly used to measure the quality of images generated by models.
+
+- Super-Resolution 
+- Image Denoising
+- Image Compression
+
+PSNR is a measure of the quality of reconstruction of an image. The higher the PSNR, the
+better the quality of the image.
 
 ## How to Use
-*Give general statement of how to use the metric*
 
-*Provide simplest possible example for using the metric*
+At minimum, this metric requires predictions and references as inputs.
+
+```python
+import evaluate
+
+psnr = evaluate.load("jpxkqx/peak_signal_to_noise_ratio")
+psnr.compute(predictions=[[0.0, 0.1], [0.1, 0.9]], references=[[0.0, 0.2], [0.1, 0.8]])
+```
 
 ### Inputs
-*List all input arguments in the format below*
-- **input_field** *(type): Definition of input, with explanation if necessary. State any default value(s).*
 
-### Output Values
+- **predictions** *('np.array'): Predictions to evaluate.*
+- **references** *('np.array'): True image to consider as baseline.*
+- **data_range** *('float'): The data range of the images (distance between the minimum
+and maximum possible values). If not provided, it is determined from the image data-type.*
+- **sample_weight** *('list'): Sample weights default to None.*
+
 
-*Explain what this metric outputs and provide an example of what the metric output looks like. Modules should return a dictionary with one or multiple key-value pairs, e.g. {"bleu" : 6.02}*
+### Output Values
 
-*State the range of possible values that the metric's output can take, as well as what in that range is considered good. For example: "This metric can take on any value between 0 and 100, inclusive. Higher scores are better."*
+- **psnr** *('float'): Peak Signal to Noise Ratio, which it is expressed as a
+logarithmic quantity using the decibel scale.*
 
-#### Values from Popular Papers
-*Give examples, preferrably with links to leaderboards or publications, to papers that have reported this metric, along with the values they have reported.*
+Outputs example:
 
-### Examples
-*Give code examples of the metric being used. Try to include examples that clear up any potential ambiguity left from the metric description above. If possible, provide a range of examples that show both typical and atypical results, as well as examples where a variety of input parameters are passed.*
+```python
+{'psnr': 35.23}
+```
+Typical values for the PSNR in lossy image and video compression are between 30 and 50
+dB, provided the bit depth is 8 bits. 
 
-## Limitations and Bias
-*Note any known limitations or biases that the metric has, with links and references if possible.*
 
-## Citation
-*Cite the source where this metric was introduced.*
 
 ## Further References
-*Add any useful further references.*
+
+
+[Peak Signal to Noise Ratio (PSNR) - Wikipedia](https://en.wikipedia.org/wiki/Peak_signal-to-noise_ratio)
+[Peak Signal to Noise Ratio (PSNR) - scikit-image](https://scikit-image.org/docs/dev/api/skimage.metrics.html#skimage.metrics.peak_signal_noise_ratio)
+[Peak Signal to Noise Ratio (PSNR) - PyTorch](https://pytorch.org/ignite/generated/ignite.metrics.PSNR.html)
+[Peak Signal to Noise Ratio (PSNR) - TensorFlow](https://www.tensorflow.org/api_docs/python/tf/image/psnr)