diff --git a/ffcv/.DS_Store b/ffcv/.DS_Store
new file mode 100644
index 00000000..f1fad9b3
Binary files /dev/null and b/ffcv/.DS_Store differ
diff --git a/ffcv/transforms/__init__.py b/ffcv/transforms/__init__.py
index bc8fa321..2636a447 100644
--- a/ffcv/transforms/__init__.py
+++ b/ffcv/transforms/__init__.py
@@ -9,6 +9,7 @@
 from .translate import RandomTranslate
 from .mixup import ImageMixup, LabelMixup, MixupToOneHot
 from .module import ModuleWrapper
+from .color_jitter import RandomBrightness, RandomContrast, RandomSaturation
 
 __all__ = ['ToTensor', 'ToDevice',
            'ToTorchImage', 'NormalizeImage',
@@ -16,4 +17,5 @@
            'RandomResizedCrop', 'RandomHorizontalFlip', 'RandomTranslate',
            'Cutout', 'ImageMixup', 'LabelMixup', 'MixupToOneHot',
            'Poison', 'ReplaceLabel',
-           'ModuleWrapper']
\ No newline at end of file
+           'ModuleWrapper',
+           'RandomBrightness', 'RandomContrast', 'RandomSaturation']
diff --git a/ffcv/transforms/color_jitter.py b/ffcv/transforms/color_jitter.py
new file mode 100644
index 00000000..a79b72fd
--- /dev/null
+++ b/ffcv/transforms/color_jitter.py
@@ -0,0 +1,139 @@
+'''
+Random color operations similar to torchvision.transforms.ColorJitter except not supporting hue
+Reference : https://github.com/pytorch/vision/blob/main/torchvision/transforms/functional_tensor.py
+'''
+
+import numpy as np
+
+from dataclasses import replace
+from ..pipeline.allocation_query import AllocationQuery
+from ..pipeline.operation import Operation
+from ..pipeline.state import State
+from ..pipeline.compiler import Compiler
+
+
+
+class RandomBrightness(Operation):
+    '''
+    Randomly adjust image brightness. Operates on raw arrays (not tensors).
+
+    Parameters
+    ----------
+    magnitude : float
+        randomly choose brightness enhancement factor on [max(0, 1-magnitude), 1+magnitude]
+    p : float
+        probability to apply brightness
+    '''
+    def __init__(self, magnitude: float, p=0.5):
+        super().__init__()
+        self.p = p
+        self.magnitude = magnitude
+
+    def generate_code(self):
+        my_range = Compiler.get_iterator()
+        p = self.p
+        magnitude = self.magnitude
+
+        def brightness(images, *_):
+            def blend(img1, img2, ratio): return (ratio*img1 + (1-ratio)*img2).clip(0, 255).astype(img1.dtype)
+
+            apply_bright = np.random.rand(images.shape[0]) < p
+            magnitudes = np.random.uniform(max(0, 1-magnitude), 1+magnitude, images.shape[0])
+            for i in my_range(images.shape[0]):
+                if apply_bright[i]:
+                    images[i] = blend(images[i], 0, magnitudes[i])
+
+            return images
+
+        brightness.is_parallel = True
+        return brightness
+
+    def declare_state_and_memory(self, previous_state):
+        return (replace(previous_state, jit_mode=True), AllocationQuery(previous_state.shape, previous_state.dtype))
+
+
+
+class RandomContrast(Operation):
+    '''
+    Randomly adjust image contrast. Operates on raw arrays (not tensors).
+
+    Parameters
+    ----------
+    magnitude : float
+        randomly choose contrast enhancement factor on [max(0, 1-magnitude), 1+magnitude]
+    p : float
+        probability to apply contrast
+    '''
+    def __init__(self, magnitude, p=0.5):
+        super().__init__()
+        self.p = p
+        self.magnitude = magnitude
+
+    def generate_code(self):
+        my_range = Compiler.get_iterator()
+        p = self.p
+        magnitude = self.magnitude
+
+        def contrast(images, *_):
+            def blend(img1, img2, ratio): return (ratio*img1 + (1-ratio)*img2).clip(0, 255).astype(img1.dtype)
+
+            apply_contrast = np.random.rand(images.shape[0]) < p
+            magnitudes = np.random.uniform(max(0, 1-magnitude), 1+magnitude, images.shape[0])
+            for i in my_range(images.shape[0]):
+                if apply_contrast[i]:
+                    r, g, b = images[i,:,:,0], images[i,:,:,1], images[i,:,:,2]
+                    l_img = (0.2989 * r + 0.587 * g + 0.114 * b).astype(images[i].dtype)
+                    images[i] = blend(images[i], l_img.mean(), magnitudes[i])
+
+            return images
+
+        contrast.is_parallel = True
+        return contrast
+
+    def declare_state_and_memory(self, previous_state):
+        return (replace(previous_state, jit_mode=True), AllocationQuery(previous_state.shape, previous_state.dtype))
+
+
+
+class RandomSaturation(Operation):
+    '''
+    Randomly adjust image color balance. Operates on raw arrays (not tensors).
+
+    Parameters
+    ----------
+    magnitude : float
+        randomly choose color balance enhancement factor on [max(0, 1-magnitude), 1+magnitude]
+    p : float
+        probability to apply saturation
+    '''
+    def __init__(self, magnitude, p=0.5):
+        super().__init__()
+        self.p = p
+        self.magnitude = magnitude
+
+    def generate_code(self):
+        my_range = Compiler.get_iterator()
+        p = self.p
+        magnitude = self.magnitude
+
+        def saturation(images, *_):
+            def blend(img1, img2, ratio): return (ratio*img1 + (1-ratio)*img2).clip(0, 255).astype(img1.dtype)
+
+            apply_saturation = np.random.rand(images.shape[0]) < p
+            magnitudes = np.random.uniform(max(0, 1-magnitude), 1+magnitude, images.shape[0])
+            for i in my_range(images.shape[0]):
+                if apply_saturation[i]:
+                    r, g, b = images[i,:,:,0], images[i,:,:,1], images[i,:,:,2]
+                    l_img = (0.2989 * r + 0.587 * g + 0.114 * b).astype(images[i].dtype)
+                    l_img3 = np.zeros_like(images[i])
+                    for j in my_range(images[i].shape[-1]):
+                        l_img3[:,:,j] = l_img
+                    images[i] = blend(images[i], l_img3, magnitudes[i])
+
+            return images
+
+        saturation.is_parallel = True
+        return saturation
+
+    def declare_state_and_memory(self, previous_state):
+        return (replace(previous_state, jit_mode=True), AllocationQuery(previous_state.shape, previous_state.dtype))