# Peaksignalnoiseratiowithblockedeffect

> Compute the PeakSignalNoiseRatioWithBlockedEffect metric — provided by torchmetrics. Use when the user has predictions and ground-truth and needs to compute PeakSignalNoiseRatioWithBlockedEffect, or asks how to score with PeakSignalNoiseRatioWithBlockedEffect.

- Skill: `qhjqhj00/peaksignalnoiseratiowithblockedeffect` (Agent Skill)
- Install (CLI): `npx skillmds add qhjqhj00/peaksignalnoiseratiowithblockedeffect`
- Raw SKILL.md: https://api.skillmd.com/api/skills/qhjqhj00/peaksignalnoiseratiowithblockedeffect/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: qhjqhj00 (https://skillmd.com/u/qhjqhj00)
- Updated: 2026-09-08
- Page: https://skillmd.com/skills/qhjqhj00/peaksignalnoiseratiowithblockedeffect

---


# peaksignalnoiseratiowithblockedeffect

> Metric `PeakSignalNoiseRatioWithBlockedEffect` from `torchmetrics` (torchmetrics.image.PeakSignalNoiseRatioWithBlockedEffect)

## When to invoke this skill

The user has predictions + ground truth and asks to evaluate with PeakSignalNoiseRatioWithBlockedEffect, or
mentions `torchmetrics.image.PeakSignalNoiseRatioWithBlockedEffect` directly, or wants the standard torchmetrics implementation.

## Reference signature

```python
from torchmetrics.image import PeakSignalNoiseRatioWithBlockedEffect

# PeakSignalNoiseRatioWithBlockedEffect(data_range: Union[float, tuple[float, float]], block_size: int = 8, **kwargs: Any) -> None
```

## Library docstring

```
Computes `Peak Signal to Noise Ratio With Blocked Effect`_ (PSNRB).

.. math::
    \text{PSNRB}(I, J) = 10 * \log_{10} \left(\frac{\max(I)^2}{\text{MSE}(I, J)-\text{B}(I, J)}\right)

Where :math:`\text{MSE}` denotes the `mean-squared-error`_ function. This metric is a modified version of PSNR that
better supports evaluation of images with blocked artifacts, that oftens occur in compressed images.

.. attention::
    Metric only supports grayscale images. If you have RGB images, please convert them to grayscale first.

As input to ``forward`` and ``update`` the metric accepts the following input

- ``preds`` (:class:`~torch.Tensor`): Predictions from model of shape ``(N,1,H,W)``
- ``target`` (:class:`~torch.Tensor`): Ground truth values of shape ``(N,1,H,W)``

As output of `forward` and `compute` the metric returns the following output

- ``psnrb`` (:class:`~torch.Tensor`): float scalar tensor with aggregated PSNRB value

Args:
    data_range: the range of the data. If a tuple is provided then the range is calculated as the difference and
        input is clamped between the values.
    block_size: integer indication the block size
    kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.

Example:
    >>> from torch import rand
    >>> metric = PeakSignalNoiseRatioWithBlockedEffect(data_range=1.0)
    >>> preds = rand(2, 1, 10, 10)
    >>> target = rand(2, 1, 10, 10)
    >>> metric(preds, target)
    tensor(7.2893)
```

## Quick recipe

```python
import torchmetrics.image as _m
score = _m.PeakSignalNoiseRatioWithBlockedEffect(y_true, y_pred)
```

## Don'ts

- Don't reimplement when the library version handles edge cases (NaN, ties, empty inputs) better than a hand-rolled formula.
- Always check the library version's argument order — sklearn is `(y_true, y_pred)` while torchmetrics is `(preds, target)`.