diff --git a/CHANGELOG.md b/CHANGELOG.md
index b26bcc71c74..9fcd0e8f1cf 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -16,7 +16,8 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 - Added support for plotting of metrics through `.plot()` method (
     [#1328](https://github.com/Lightning-AI/metrics/pull/1328),
-    [#1481](https://github.com/Lightning-AI/metrics/pull/1481)
+    [#1481](https://github.com/Lightning-AI/metrics/pull/1481),
+    [#1480](https://github.com/Lightning-AI/metrics/pull/1480)
 )
 
 
diff --git a/examples/plotting.py b/examples/plotting.py
index cc85c091ebf..c613f4937d3 100644
--- a/examples/plotting.py
+++ b/examples/plotting.py
@@ -222,8 +222,147 @@ def confusion_matrix_example():
     return fig, ax
 
 
-if __name__ == "__main__":
+def spectral_distortion_index_example():
+    """Plot spectral distortion index example example."""
+    from torchmetrics.image.d_lambda import SpectralDistortionIndex
+
+    p = lambda: torch.rand([16, 3, 16, 16])
+    t = lambda: torch.rand([16, 3, 16, 16])
+
+    # plot single value
+    metric = SpectralDistortionIndex()
+    metric.update(p(), t())
+    fig, ax = metric.plot()
+
+    # plot multiple values
+    metric = SpectralDistortionIndex()
+    vals = [metric(p(), t()) for _ in range(10)]
+    fig, ax = metric.plot(vals)
+
+    return fig, ax
+
+
+def error_relative_global_dimensionless_synthesis():
+    """Plot error relative global dimensionless synthesis example."""
+    from torchmetrics.image.ergas import ErrorRelativeGlobalDimensionlessSynthesis
+
+    p = lambda: torch.rand([16, 1, 16, 16], generator=torch.manual_seed(42))
+    t = lambda: torch.rand([16, 1, 16, 16], generator=torch.manual_seed(42))
+
+    # plot single value
+    metric = ErrorRelativeGlobalDimensionlessSynthesis()
+    metric.update(p(), t())
+    fig, ax = metric.plot()
+
+    # plot multiple values
+    metric = ErrorRelativeGlobalDimensionlessSynthesis()
+    vals = [metric(p(), t()) for _ in range(10)]
+    fig, ax = metric.plot(vals)
+
+    return fig, ax
+
+
+def peak_signal_noise_ratio():
+    """Plot peak signal noise ratio example."""
+    from torchmetrics.image.psnr import PeakSignalNoiseRatio
+
+    p = lambda: torch.tensor([[0.0, 1.0], [2.0, 3.0]])
+    t = lambda: torch.tensor([[3.0, 2.0], [1.0, 0.0]])
+
+    # plot single value
+    metric = PeakSignalNoiseRatio()
+    metric.update(p(), t())
+    fig, ax = metric.plot()
+
+    # plot multiple values
+    metric = PeakSignalNoiseRatio()
+    vals = [metric(p(), t()) for _ in range(10)]
+    fig, ax = metric.plot(vals)
+
+    return fig, ax
+
+
+def spectral_angle_mapper():
+    """Plot spectral angle mapper example."""
+    from torchmetrics.image.sam import SpectralAngleMapper
+
+    p = lambda: torch.rand([16, 3, 16, 16], generator=torch.manual_seed(42))
+    t = lambda: torch.rand([16, 3, 16, 16], generator=torch.manual_seed(123))
+
+    # plot single value
+    metric = SpectralAngleMapper()
+    metric.update(p(), t())
+    fig, ax = metric.plot()
+
+    # plot multiple values
+    metric = SpectralAngleMapper()
+    vals = [metric(p(), t()) for _ in range(10)]
+    fig, ax = metric.plot(vals)
+
+    return fig, ax
+
+
+def structural_similarity_index_measure():
+    """Plot structural similarity index measure example."""
+    from torchmetrics.image.ssim import StructuralSimilarityIndexMeasure
+
+    p = lambda: torch.rand([3, 3, 256, 256], generator=torch.manual_seed(42))
+    t = lambda: p() * 0.75
+
+    # plot single value
+    metric = StructuralSimilarityIndexMeasure()
+    metric.update(p(), t())
+    fig, ax = metric.plot()
+
+    # plot multiple values
+    metric = StructuralSimilarityIndexMeasure()
+    vals = [metric(p(), t()) for _ in range(10)]
+    fig, ax = metric.plot(vals)
+
+    return fig, ax
+
+
+def multiscale_structural_similarity_index_measure():
+    """Plot multiscale structural similarity index measure example."""
+    from torchmetrics.image.ssim import MultiScaleStructuralSimilarityIndexMeasure
+
+    p = lambda: torch.rand([3, 3, 256, 256], generator=torch.manual_seed(42))
+    t = lambda: p() * 0.75
+
+    # plot single value
+    metric = MultiScaleStructuralSimilarityIndexMeasure()
+    metric.update(p(), t())
+    fig, ax = metric.plot()
+
+    # plot multiple values
+    metric = MultiScaleStructuralSimilarityIndexMeasure()
+    vals = [metric(p(), t()) for _ in range(10)]
+    fig, ax = metric.plot(vals)
 
+    return fig, ax
+
+
+def universal_image_quality_index():
+    """Plot universal image quality index example."""
+    from torchmetrics.image.uqi import UniversalImageQualityIndex
+
+    p = lambda: torch.rand([16, 1, 16, 16])
+    t = lambda: p() * 0.75
+
+    # plot single value
+    metric = UniversalImageQualityIndex()
+    metric.update(p(), t())
+    fig, ax = metric.plot()
+
+    # plot multiple values
+    metric = UniversalImageQualityIndex()
+    vals = [metric(p(), t()) for _ in range(10)]
+    fig, ax = metric.plot(vals)
+
+    return fig, ax
+
+
+if __name__ == "__main__":
     metrics_func = {
         "accuracy": accuracy_example,
         "pesq": pesq_example,
@@ -235,6 +374,13 @@ def confusion_matrix_example():
         "stoi": stoi_example,
         "mean_squared_error": mean_squared_error_example,
         "confusion_matrix": confusion_matrix_example,
+        "spectral_distortion_index": spectral_distortion_index_example,
+        "error_relative_global_dimensionless_synthesis": error_relative_global_dimensionless_synthesis,
+        "peak_signal_noise_ratio": peak_signal_noise_ratio,
+        "spectral_angle_mapper": spectral_angle_mapper,
+        "structural_similarity_index_measure": structural_similarity_index_measure,
+        "multiscale_structural_similarity_index_measure": multiscale_structural_similarity_index_measure,
+        "universal_image_quality_index": universal_image_quality_index,
     }
 
     parser = argparse.ArgumentParser(description="Example script for plotting metrics.")
diff --git a/src/torchmetrics/classification/accuracy.py b/src/torchmetrics/classification/accuracy.py
index 7960029a095..3ec993f6b6d 100644
--- a/src/torchmetrics/classification/accuracy.py
+++ b/src/torchmetrics/classification/accuracy.py
@@ -414,8 +414,7 @@ def plot(
             ax: An matplotlib axis object. If provided will add plot to that axis
 
         Returns:
-            fig: Figure object
-            ax: Axes object
+            Figure and Axes object
 
         Raises:
             ModuleNotFoundError:
diff --git a/src/torchmetrics/classification/confusion_matrix.py b/src/torchmetrics/classification/confusion_matrix.py
index ed37d9e451d..35b11782f55 100644
--- a/src/torchmetrics/classification/confusion_matrix.py
+++ b/src/torchmetrics/classification/confusion_matrix.py
@@ -239,8 +239,7 @@ def plot(self, val: Optional[Tensor] = None) -> _PLOT_OUT_TYPE:
                 If no value is provided, will automatically call `metric.compute` and plot that result.
 
         Returns:
-            fig: Figure object
-            ax: Axes object
+            Figure and Axes object
 
         Raises:
             ModuleNotFoundError:
diff --git a/src/torchmetrics/image/d_lambda.py b/src/torchmetrics/image/d_lambda.py
index 20367cf2201..4386e23f849 100644
--- a/src/torchmetrics/image/d_lambda.py
+++ b/src/torchmetrics/image/d_lambda.py
@@ -11,7 +11,7 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-from typing import Any, List
+from typing import Any, Dict, List, Optional, Sequence, Union
 
 from torch import Tensor
 from typing_extensions import Literal
@@ -20,6 +20,11 @@
 from torchmetrics.metric import Metric
 from torchmetrics.utilities import rank_zero_warn
 from torchmetrics.utilities.data import dim_zero_cat
+from torchmetrics.utilities.imports import _MATPLOTLIB_AVAILABLE
+from torchmetrics.utilities.plot import _AX_TYPE, _PLOT_OUT_TYPE, plot_single_or_multi_val
+
+if not _MATPLOTLIB_AVAILABLE:
+    __doctest_skip__ = ["SpectralDistortionIndex.plot"]
 
 
 class SpectralDistortionIndex(Metric):
@@ -95,3 +100,59 @@ def compute(self) -> Tensor:
         preds = dim_zero_cat(self.preds)
         target = dim_zero_cat(self.target)
         return _spectral_distortion_index_compute(preds, target, self.p, self.reduction)
+
+    def plot(
+        self, val: Optional[Union[Tensor, Sequence[Tensor]]] = None, ax: Optional[_AX_TYPE] = None
+    ) -> _PLOT_OUT_TYPE:
+        """Plot a single or multiple values from the metric.
+
+        Args:
+            val: Either a single result from calling `metric.forward` or `metric.compute` or a list of these results.
+                If no value is provided, will automatically call `metric.compute` and plot that result.
+            ax: An matplotlib axis object. If provided will add plot to that axis
+
+        Returns:
+            Figure and Axes object
+
+        Raises:
+            ModuleNotFoundError:
+                If `matplotlib` is not installed
+
+        .. plot::
+            :scale: 75
+
+            >>> # Example plotting a single value
+            >>> import torch
+            >>> _ = torch.manual_seed(42)
+            >>> from torchmetrics import SpectralDistortionIndex
+            >>> preds = torch.rand([16, 3, 16, 16])
+            >>> target = torch.rand([16, 3, 16, 16])
+            >>> metric = SpectralDistortionIndex()
+            >>> metric.update(preds, target)
+            >>> fig_, ax_ = metric.plot()
+
+        .. plot::
+            :scale: 75
+
+            >>> # Example plotting multiple values
+            >>> import torch
+            >>> _ = torch.manual_seed(42)
+            >>> from torchmetrics import SpectralDistortionIndex
+            >>> preds = torch.rand([16, 3, 16, 16])
+            >>> target = torch.rand([16, 3, 16, 16])
+            >>> metric = SpectralDistortionIndex()
+            >>> values = [ ]
+            >>> for _ in range(10):
+            ...     values.append(metric(preds, target))
+            >>> fig_, ax_ = metric.plot(values)
+        """
+        val = val or self.compute()
+        fig, ax = plot_single_or_multi_val(
+            val,
+            ax=ax,
+            higher_is_better=self.higher_is_better,
+            name=self.__class__.__name__,
+            lower_bound=0.0,
+            upper_bound=1.0,
+        )
+        return fig, ax
diff --git a/src/torchmetrics/image/ergas.py b/src/torchmetrics/image/ergas.py
index 051a42bc38d..1c71a42540e 100644
--- a/src/torchmetrics/image/ergas.py
+++ b/src/torchmetrics/image/ergas.py
@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from typing import Any, List, Union
+from typing import Any, Dict, List, Optional, Sequence, Union
 
 from torch import Tensor
 from typing_extensions import Literal
@@ -21,6 +21,11 @@
 from torchmetrics.metric import Metric
 from torchmetrics.utilities import rank_zero_warn
 from torchmetrics.utilities.data import dim_zero_cat
+from torchmetrics.utilities.imports import _MATPLOTLIB_AVAILABLE
+from torchmetrics.utilities.plot import _AX_TYPE, _PLOT_OUT_TYPE, plot_single_or_multi_val
+
+if not _MATPLOTLIB_AVAILABLE:
+    __doctest_skip__ = ["ErrorRelativeGlobalDimensionlessSynthesis.plot"]
 
 
 class ErrorRelativeGlobalDimensionlessSynthesis(Metric):
@@ -94,3 +99,57 @@ def compute(self) -> Tensor:
         preds = dim_zero_cat(self.preds)
         target = dim_zero_cat(self.target)
         return _ergas_compute(preds, target, self.ratio, self.reduction)
+
+    def plot(
+        self, val: Optional[Union[Tensor, Sequence[Tensor]]] = None, ax: Optional[_AX_TYPE] = None
+    ) -> _PLOT_OUT_TYPE:
+        """Plot a single or multiple values from the metric.
+
+        Args:
+            val: Either a single result from calling `metric.forward` or `metric.compute` or a list of these results.
+                If no value is provided, will automatically call `metric.compute` and plot that result.
+            ax: An matplotlib axis object. If provided will add plot to that axis
+
+        Returns:
+            Figure and Axes object
+
+        Raises:
+            ModuleNotFoundError:
+                If `matplotlib` is not installed
+
+        .. plot::
+            :scale: 75
+
+            >>> # Example plotting a single value
+            >>> import torch
+            >>> from torchmetrics import ErrorRelativeGlobalDimensionlessSynthesis
+            >>> preds = torch.rand([16, 1, 16, 16], generator=torch.manual_seed(42))
+            >>> target = preds * 0.75
+            >>> metric = ErrorRelativeGlobalDimensionlessSynthesis()
+            >>> metric.update(preds, target)
+            >>> fig_, ax_ = metric.plot()
+
+        .. plot::
+            :scale: 75
+
+            >>> # Example plotting multiple values
+            >>> import torch
+            >>> from torchmetrics import ErrorRelativeGlobalDimensionlessSynthesis
+            >>> preds = torch.rand([16, 1, 16, 16], generator=torch.manual_seed(42))
+            >>> target = preds * 0.75
+            >>> metric = ErrorRelativeGlobalDimensionlessSynthesis()
+            >>> values = [ ]
+            >>> for _ in range(10):
+            ...     values.append(metric(preds, target))
+            >>> fig_, ax_ = metric.plot(values)
+        """
+        val = val or self.compute()
+        fig, ax = plot_single_or_multi_val(
+            val,
+            ax=ax,
+            higher_is_better=self.higher_is_better,
+            name=self.__class__.__name__,
+            lower_bound=0.0,
+            upper_bound=1.0,
+        )
+        return fig, ax
diff --git a/src/torchmetrics/image/psnr.py b/src/torchmetrics/image/psnr.py
index dc23664375d..a0dd7097f1f 100644
--- a/src/torchmetrics/image/psnr.py
+++ b/src/torchmetrics/image/psnr.py
@@ -20,6 +20,11 @@
 from torchmetrics.functional.image.psnr import _psnr_compute, _psnr_update
 from torchmetrics.metric import Metric
 from torchmetrics.utilities import rank_zero_warn
+from torchmetrics.utilities.imports import _MATPLOTLIB_AVAILABLE
+from torchmetrics.utilities.plot import _AX_TYPE, _PLOT_OUT_TYPE, plot_single_or_multi_val
+
+if not _MATPLOTLIB_AVAILABLE:
+    __doctest_skip__ = ["PeakSignalNoiseRatio.plot"]
 
 
 class PeakSignalNoiseRatio(Metric):
@@ -70,6 +75,7 @@ class PeakSignalNoiseRatio(Metric):
     is_differentiable: bool = True
     higher_is_better: bool = True
     full_state_update: bool = False
+    plot_options = {"lower_bound": 0.0, "upper_bound": 10.0}
 
     min_target: Tensor
     max_target: Tensor
@@ -135,3 +141,52 @@ def compute(self) -> Tensor:
             sum_squared_error = torch.cat([values.flatten() for values in self.sum_squared_error])
             total = torch.cat([values.flatten() for values in self.total])
         return _psnr_compute(sum_squared_error, total, data_range, base=self.base, reduction=self.reduction)
+
+    def plot(
+        self, val: Optional[Union[Tensor, Sequence[Tensor]]] = None, ax: Optional[_AX_TYPE] = None
+    ) -> _PLOT_OUT_TYPE:
+        """Plot a single or multiple values from the metric.
+
+        Args:
+            val: Either a single result from calling `metric.forward` or `metric.compute` or a list of these results.
+                If no value is provided, will automatically call `metric.compute` and plot that result.
+            ax: An matplotlib axis object. If provided will add plot to that axis
+
+        Returns:
+            Figure and Axes object
+
+        Raises:
+            ModuleNotFoundError:
+                If `matplotlib` is not installed
+
+        .. plot::
+            :scale: 75
+
+            >>> # Example plotting a single value
+            >>> import torch
+            >>> from torchmetrics import PeakSignalNoiseRatio
+            >>> metric = PeakSignalNoiseRatio()
+            >>> preds = torch.tensor([[0.0, 1.0], [2.0, 3.0]])
+            >>> target = torch.tensor([[3.0, 2.0], [1.0, 0.0]])
+            >>> metric.update(preds, target)
+            >>> fig_, ax_ = metric.plot()
+
+        .. plot::
+            :scale: 75
+
+            >>> # Example plotting multiple values
+            >>> import torch
+            >>> from torchmetrics import PeakSignalNoiseRatio
+            >>> metric = PeakSignalNoiseRatio()
+            >>> preds = torch.tensor([[0.0, 1.0], [2.0, 3.0]])
+            >>> target = torch.tensor([[3.0, 2.0], [1.0, 0.0]])
+            >>> values = [ ]
+            >>> for _ in range(10):
+            ...     values.append(metric(preds, target))
+            >>> fig_, ax_ = metric.plot(values)
+        """
+        val = val or self.compute()
+        fig, ax = plot_single_or_multi_val(
+            val, ax=ax, higher_is_better=self.higher_is_better, **self.plot_options, name=self.__class__.__name__
+        )
+        return fig, ax
diff --git a/src/torchmetrics/image/sam.py b/src/torchmetrics/image/sam.py
index c0e064c2ebb..1d1611a5afd 100644
--- a/src/torchmetrics/image/sam.py
+++ b/src/torchmetrics/image/sam.py
@@ -11,7 +11,7 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-from typing import Any, List
+from typing import Any, Dict, List, Optional, Sequence, Union
 
 from torch import Tensor
 from typing_extensions import Literal
@@ -20,6 +20,11 @@
 from torchmetrics.metric import Metric
 from torchmetrics.utilities import rank_zero_warn
 from torchmetrics.utilities.data import dim_zero_cat
+from torchmetrics.utilities.imports import _MATPLOTLIB_AVAILABLE
+from torchmetrics.utilities.plot import _AX_TYPE, _PLOT_OUT_TYPE, plot_single_or_multi_val
+
+if not _MATPLOTLIB_AVAILABLE:
+    __doctest_skip__ = ["SpectralAngleMapper.plot"]
 
 
 class SpectralAngleMapper(Metric):
@@ -92,3 +97,57 @@ def compute(self) -> Tensor:
         preds = dim_zero_cat(self.preds)
         target = dim_zero_cat(self.target)
         return _sam_compute(preds, target, self.reduction)
+
+    def plot(
+        self, val: Optional[Union[Tensor, Sequence[Tensor]]] = None, ax: Optional[_AX_TYPE] = None
+    ) -> _PLOT_OUT_TYPE:
+        """Plot a single or multiple values from the metric.
+
+        Args:
+            val: Either a single result from calling `metric.forward` or `metric.compute` or a list of these results.
+                If no value is provided, will automatically call `metric.compute` and plot that result.
+            ax: An matplotlib axis object. If provided will add plot to that axis
+
+        Returns:
+            Figure and Axes object
+
+        Raises:
+            ModuleNotFoundError:
+                If `matplotlib` is not installed
+
+        .. plot::
+            :scale: 75
+
+            >>> # Example plotting single value
+            >>> import torch
+            >>> from torchmetrics import SpectralAngleMapper
+            >>> preds = torch.rand([16, 3, 16, 16], generator=torch.manual_seed(42))
+            >>> target = torch.rand([16, 3, 16, 16], generator=torch.manual_seed(123))
+            >>> metric = SpectralAngleMapper()
+            >>> metric.update(preds, target)
+            >>> fig_, ax_ = metric.plot()
+
+        .. plot::
+            :scale: 75
+
+            >>> # Example plotting multiple values
+            >>> import torch
+            >>> from torchmetrics import SpectralAngleMapper
+            >>> preds = torch.rand([16, 3, 16, 16], generator=torch.manual_seed(42))
+            >>> target = torch.rand([16, 3, 16, 16], generator=torch.manual_seed(123))
+            >>> metric = SpectralAngleMapper()
+            >>> values = [ ]
+            >>> for _ in range(10):
+            ...     values.append(metric(preds, target))
+            >>> fig_, ax_ = metric.plot(values)
+        """
+        val = val or self.compute()
+        fig, ax = plot_single_or_multi_val(
+            val,
+            ax=ax,
+            higher_is_better=self.higher_is_better,
+            name=self.__class__.__name__,
+            lower_bound=0.0,
+            upper_bound=1.0,
+        )
+        return fig, ax
diff --git a/src/torchmetrics/image/ssim.py b/src/torchmetrics/image/ssim.py
index 0751a0e9f26..6a1d41d36aa 100644
--- a/src/torchmetrics/image/ssim.py
+++ b/src/torchmetrics/image/ssim.py
@@ -20,6 +20,11 @@
 from torchmetrics.functional.image.ssim import _multiscale_ssim_update, _ssim_check_inputs, _ssim_update
 from torchmetrics.metric import Metric
 from torchmetrics.utilities.data import dim_zero_cat
+from torchmetrics.utilities.imports import _MATPLOTLIB_AVAILABLE
+from torchmetrics.utilities.plot import _AX_TYPE, _PLOT_OUT_TYPE, plot_single_or_multi_val
+
+if not _MATPLOTLIB_AVAILABLE:
+    __doctest_skip__ = ["StructuralSimilarityIndexMeasure.plot", "MultiScaleStructuralSimilarityIndexMeasure.plot"]
 
 
 class StructuralSimilarityIndexMeasure(Metric):
@@ -72,6 +77,7 @@ class StructuralSimilarityIndexMeasure(Metric):
     higher_is_better: bool = True
     is_differentiable: bool = True
     full_state_update: bool = False
+    plot_options = {"lower_bound": 0.0, "upper_bound": 1.0}
 
     preds: List[Tensor]
     target: List[Tensor]
@@ -160,6 +166,55 @@ def compute(self) -> Union[Tensor, Tuple[Tensor, Tensor]]:
 
         return similarity
 
+    def plot(
+        self, val: Optional[Union[Tensor, Sequence[Tensor]]] = None, ax: Optional[_AX_TYPE] = None
+    ) -> _PLOT_OUT_TYPE:
+        """Plot a single or multiple values from the metric.
+
+        Args:
+            val: Either a single result from calling `metric.forward` or `metric.compute` or a list of these results.
+                If no value is provided, will automatically call `metric.compute` and plot that result.
+            ax: An matplotlib axis object. If provided will add plot to that axis
+
+        Returns:
+            Figure and Axes object
+
+        Raises:
+            ModuleNotFoundError:
+                If `matplotlib` is not installed
+
+        .. plot::
+            :scale: 75
+
+            >>> # Example plotting a single value
+            >>> import torch
+            >>> from torchmetrics import StructuralSimilarityIndexMeasure
+            >>> preds = torch.rand([3, 3, 256, 256])
+            >>> target = preds * 0.75
+            >>> metric = StructuralSimilarityIndexMeasure(data_range=1.0)
+            >>> metric.update(preds, target)
+            >>> fig_, ax_ = metric.plot()
+
+        .. plot::
+            :scale: 75
+
+            >>> # Example plotting multiple values
+            >>> import torch
+            >>> from torchmetrics import StructuralSimilarityIndexMeasure
+            >>> preds = torch.rand([3, 3, 256, 256])
+            >>> target = preds * 0.75
+            >>> metric = StructuralSimilarityIndexMeasure(data_range=1.0)
+            >>> values = [ ]
+            >>> for _ in range(10):
+            ...     values.append(metric(preds, target))
+            >>> fig_, ax_ = metric.plot(values)
+        """
+        val = val or self.compute()
+        fig, ax = plot_single_or_multi_val(
+            val, ax=ax, higher_is_better=self.higher_is_better, **self.plot_options, name=self.__class__.__name__
+        )
+        return fig, ax
+
 
 class MultiScaleStructuralSimilarityIndexMeasure(Metric):
     """Compute `MultiScaleSSIM`_, Multi-scale Structural Similarity Index Measure, which is a generalization of
@@ -219,6 +274,7 @@ class MultiScaleStructuralSimilarityIndexMeasure(Metric):
     higher_is_better: bool = True
     is_differentiable: bool = True
     full_state_update: bool = False
+    plot_options = {"lower_bound": 0.0, "upper_bound": 1.0}
 
     preds: List[Tensor]
     target: List[Tensor]
@@ -308,3 +364,52 @@ def compute(self) -> Tensor:
             return self.similarity
         else:
             return self.similarity / self.total
+
+    def plot(
+        self, val: Optional[Union[Tensor, Sequence[Tensor]]] = None, ax: Optional[_AX_TYPE] = None
+    ) -> _PLOT_OUT_TYPE:
+        """Plot a single or multiple values from the metric.
+
+        Args:
+            val: Either a single result from calling `metric.forward` or `metric.compute` or a list of these results.
+                If no value is provided, will automatically call `metric.compute` and plot that result.
+            ax: An matplotlib axis object. If provided will add plot to that axis
+
+        Returns:
+            Figure and Axes object
+
+        Raises:
+            ModuleNotFoundError:
+                If `matplotlib` is not installed
+
+        .. plot::
+            :scale: 75
+
+            >>> # Example plotting a single value
+            >>> from torchmetrics import MultiScaleStructuralSimilarityIndexMeasure
+            >>> import torch
+            >>> preds = torch.rand([3, 3, 256, 256], generator=torch.manual_seed(42))
+            >>> target = preds * 0.75
+            >>> metric = MultiScaleStructuralSimilarityIndexMeasure(data_range=1.0)
+            >>> metric.update(preds, target)
+            >>> fig_, ax_ = metric.plot()
+
+        .. plot::
+            :scale: 75
+
+            >>> # Example plotting multiple values
+            >>> from torchmetrics import MultiScaleStructuralSimilarityIndexMeasure
+            >>> import torch
+            >>> preds = torch.rand([3, 3, 256, 256], generator=torch.manual_seed(42))
+            >>> target = preds * 0.75
+            >>> metric = MultiScaleStructuralSimilarityIndexMeasure(data_range=1.0)
+            >>> values = [ ]
+            >>> for _ in range(10):
+            ...     values.append(metric(preds, target))
+            >>> fig_, ax_ = metric.plot(values)
+        """
+        val = val or self.compute()
+        fig, ax = plot_single_or_multi_val(
+            val, ax=ax, higher_is_better=self.higher_is_better, **self.plot_options, name=self.__class__.__name__
+        )
+        return fig, ax
diff --git a/src/torchmetrics/image/uqi.py b/src/torchmetrics/image/uqi.py
index 0a40004ebbd..d48fe54a76f 100644
--- a/src/torchmetrics/image/uqi.py
+++ b/src/torchmetrics/image/uqi.py
@@ -11,7 +11,7 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-from typing import Any, List, Optional, Sequence
+from typing import Any, List, Optional, Sequence, Union
 
 from torch import Tensor
 from typing_extensions import Literal
@@ -20,6 +20,11 @@
 from torchmetrics.metric import Metric
 from torchmetrics.utilities import rank_zero_warn
 from torchmetrics.utilities.data import dim_zero_cat
+from torchmetrics.utilities.imports import _MATPLOTLIB_AVAILABLE
+from torchmetrics.utilities.plot import _AX_TYPE, _PLOT_OUT_TYPE, plot_single_or_multi_val
+
+if not _MATPLOTLIB_AVAILABLE:
+    __doctest_skip__ = ["UniversalImageQualityIndex.plot"]
 
 
 class UniversalImageQualityIndex(Metric):
@@ -64,6 +69,7 @@ class UniversalImageQualityIndex(Metric):
     is_differentiable: bool = True
     higher_is_better: bool = True
     full_state_update: bool = False
+    plot_options = {"lower_bound": 0.0, "upper_bound": 1.0}
 
     preds: List[Tensor]
     target: List[Tensor]
@@ -101,3 +107,52 @@ def compute(self) -> Tensor:
         preds = dim_zero_cat(self.preds)
         target = dim_zero_cat(self.target)
         return _uqi_compute(preds, target, self.kernel_size, self.sigma, self.reduction, self.data_range)
+
+    def plot(
+        self, val: Optional[Union[Tensor, Sequence[Tensor]]] = None, ax: Optional[_AX_TYPE] = None
+    ) -> _PLOT_OUT_TYPE:
+        """Plot a single or multiple values from the metric.
+
+        Args:
+            val: Either a single result from calling `metric.forward` or `metric.compute` or a list of these results.
+                If no value is provided, will automatically call `metric.compute` and plot that result.
+            ax: An matplotlib axis object. If provided will add plot to that axis
+
+        Returns:
+            Figure and Axes object
+
+        Raises:
+            ModuleNotFoundError:
+                If `matplotlib` is not installed
+
+        .. plot::
+            :scale: 75
+
+            >>> # Example plotting a single value
+            >>> import torch
+            >>> from torchmetrics import UniversalImageQualityIndex
+            >>> preds = torch.rand([16, 1, 16, 16])
+            >>> target = preds * 0.75
+            >>> metric = UniversalImageQualityIndex()
+            >>> metric.update(preds, target)
+            >>> fig_, ax_ = metric.plot()
+
+        .. plot::
+            :scale: 75
+
+            >>> # Example plotting multiple values
+            >>> import torch
+            >>> from torchmetrics import UniversalImageQualityIndex
+            >>> preds = torch.rand([16, 1, 16, 16])
+            >>> target = preds * 0.75
+            >>> metric = UniversalImageQualityIndex()
+            >>> values = [ ]
+            >>> for _ in range(10):
+            ...     values.append(metric(preds, target))
+            >>> fig_, ax_ = metric.plot(values)
+        """
+        val = val or self.compute()
+        fig, ax = plot_single_or_multi_val(
+            val, ax=ax, higher_is_better=self.higher_is_better, **self.plot_options, name=self.__class__.__name__
+        )
+        return fig, ax
diff --git a/src/torchmetrics/regression/mse.py b/src/torchmetrics/regression/mse.py
index 524b3e86499..1b97c3b391e 100644
--- a/src/torchmetrics/regression/mse.py
+++ b/src/torchmetrics/regression/mse.py
@@ -93,8 +93,7 @@ def plot(
             ax: An matplotlib axis object. If provided will add plot to that axis
 
         Returns:
-            fig: Figure object
-            ax: Axes object
+            Figure and Axes object
 
         Raises:
             ModuleNotFoundError:
diff --git a/tests/unittests/utilities/test_plot.py b/tests/unittests/utilities/test_plot.py
index 11a757f6d1e..f9d251c49ea 100644
--- a/tests/unittests/utilities/test_plot.py
+++ b/tests/unittests/utilities/test_plot.py
@@ -20,10 +20,15 @@
 import torch
 
 from torchmetrics.functional import (
+    multiscale_structural_similarity_index_measure,
+    peak_signal_noise_ratio,
     scale_invariant_signal_distortion_ratio,
     scale_invariant_signal_noise_ratio,
     signal_distortion_ratio,
     signal_noise_ratio,
+    spectral_angle_mapper,
+    structural_similarity_index_measure,
+    universal_image_quality_index,
 )
 from torchmetrics.functional.audio import short_time_objective_intelligibility
 from torchmetrics.functional.audio.pesq import perceptual_evaluation_speech_quality
@@ -34,18 +39,15 @@
     multiclass_confusion_matrix,
     multilabel_confusion_matrix,
 )
+from torchmetrics.functional.image.d_lambda import spectral_distortion_index
+from torchmetrics.functional.image.ergas import error_relative_global_dimensionless_synthesis
 from torchmetrics.utilities.plot import plot_confusion_matrix, plot_single_or_multi_val
 
 
 @pytest.mark.parametrize(
     ("metric", "preds", "target"),
     [
-        pytest.param(
-            binary_accuracy,
-            lambda: torch.rand(100),
-            lambda: torch.randint(2, (100,)),
-            id="binary",
-        ),
+        pytest.param(binary_accuracy, lambda: torch.rand(100), lambda: torch.randint(2, (100,)), id="binary"),
         pytest.param(
             partial(multiclass_accuracy, num_classes=3),
             lambda: torch.randint(3, (100,)),
@@ -58,6 +60,48 @@
             lambda: torch.randint(3, (100,)),
             id="multiclass and average=None",
         ),
+        pytest.param(
+            partial(spectral_distortion_index),
+            lambda: torch.rand([16, 3, 16, 16]),
+            lambda: torch.rand([16, 3, 16, 16]),
+            id="spectral distortion index",
+        ),
+        pytest.param(
+            partial(error_relative_global_dimensionless_synthesis),
+            lambda: torch.rand([16, 1, 16, 16], generator=torch.manual_seed(42)),
+            lambda: torch.rand([16, 1, 16, 16], generator=torch.manual_seed(42)),
+            id="error relative global dimensionless synthesis",
+        ),
+        pytest.param(
+            partial(peak_signal_noise_ratio),
+            lambda: torch.tensor([[0.0, 1.0], [2.0, 3.0]]),
+            lambda: torch.tensor([[3.0, 2.0], [1.0, 0.0]]),
+            id="peak signal noise ratio",
+        ),
+        pytest.param(
+            partial(spectral_angle_mapper),
+            lambda: torch.rand([16, 3, 16, 16], generator=torch.manual_seed(42)),
+            lambda: torch.rand([16, 3, 16, 16], generator=torch.manual_seed(123)),
+            id="spectral angle mapper",
+        ),
+        pytest.param(
+            partial(structural_similarity_index_measure),
+            lambda: torch.rand([3, 3, 256, 256], generator=torch.manual_seed(42)),
+            lambda: torch.rand([3, 3, 256, 256], generator=torch.manual_seed(42)) * 0.75,
+            id="structural similarity index_measure",
+        ),
+        pytest.param(
+            partial(multiscale_structural_similarity_index_measure),
+            lambda: torch.rand([3, 3, 256, 256], generator=torch.manual_seed(42)),
+            lambda: torch.rand([3, 3, 256, 256], generator=torch.manual_seed(42)) * 0.75,
+            id="multiscale structural similarity index measure",
+        ),
+        pytest.param(
+            partial(universal_image_quality_index),
+            lambda: torch.rand([16, 1, 16, 16]),
+            lambda: torch.rand([16, 1, 16, 16]) * 0.75,
+            id="universal image quality index",
+        ),
         pytest.param(
             partial(perceptual_evaluation_speech_quality, fs=8000, mode="nb"),
             lambda: torch.randn(8000),