diff --git a/tests/test_optimizers.py b/tests/test_optimizers.py
index a8a8bf0d2..ef9a6bec2 100644
--- a/tests/test_optimizers.py
+++ b/tests/test_optimizers.py
@@ -191,6 +191,86 @@ def test_transpose_leaky_relu(self):
         self.run_transpose_compare(["Z1"], {"X": np.random.randn(2, 3, 4, 5).astype(np.float32)},
                                    model_proto, remaining_transpose_num=0)
 
+    @check_opset_min_version(10, "QuantizeLinear")
+    def test_transpose_quantize(self):
+        scale = numpy_helper.from_array(np.array(0.75, dtype=np.float32), name='scale')
+        zero_point = numpy_helper.from_array(np.array(3, dtype=np.uint8), name='zero_point')
+        node1 = helper.make_node("Transpose", ["X"], ["Y"], perm=[0, 2, 3, 1], name="trans_1")
+        node2 = helper.make_node("QuantizeLinear", ["Y", "scale", "zero_point"], ["Z"], name="quantize")
+        node3 = helper.make_node("Transpose", ["Z"], ["Z1"], perm=[0, 3, 1, 2], name="trans_2")
+
+        graph = helper.make_graph(
+            [node1, node2, node3],
+            "quantize-test",
+            [helper.make_tensor_value_info("X", TensorProto.FLOAT, (2, 3, 4, 5))],
+            [helper.make_tensor_value_info("Z1", TensorProto.UINT8, (2, 3, 4, 5))],
+            [scale, zero_point]
+        )
+
+        model_proto = self.make_model(graph, producer_name="onnx-tests")
+        self.run_transpose_compare(["Z1"], {"X": np.random.randn(2, 3, 4, 5).astype(np.float32)},
+                                   model_proto, remaining_transpose_num=0)
+
+    @check_opset_min_version(13, "QuantizeLinear with axis")
+    def test_transpose_quantize_with_axis(self):
+        scale = numpy_helper.from_array(np.array([0.75, 0.1, 2.3, 0.3, 0.42], dtype=np.float32), name='scale')
+        zero_point = numpy_helper.from_array(np.array([2, 4, 6, 8, 10], dtype=np.uint8), name='zero_point')
+        node1 = helper.make_node("Transpose", ["X"], ["Y"], perm=[0, 2, 3, 1], name="trans_1")
+        node2 = helper.make_node("QuantizeLinear", ["Y", "scale", "zero_point"], ["Z"], name="quantize", axis=2)
+        node3 = helper.make_node("Transpose", ["Z"], ["Z1"], perm=[0, 3, 1, 2], name="trans_2")
+
+        graph = helper.make_graph(
+            [node1, node2, node3],
+            "quantize-test",
+            [helper.make_tensor_value_info("X", TensorProto.FLOAT, (2, 3, 4, 5))],
+            [helper.make_tensor_value_info("Z1", TensorProto.UINT8, (2, 3, 4, 5))],
+            [scale, zero_point]
+        )
+
+        model_proto = self.make_model(graph, producer_name="onnx-tests")
+        self.run_transpose_compare(["Z1"], {"X": np.random.randn(2, 3, 4, 5).astype(np.float32)},
+                                   model_proto, remaining_transpose_num=0)
+
+    @check_opset_min_version(10, "DequantizeLinear")
+    def test_transpose_dequantize(self):
+        scale = numpy_helper.from_array(np.array(0.75, dtype=np.float32), name='scale')
+        zero_point = numpy_helper.from_array(np.array(3, dtype=np.uint8), name='zero_point')
+        node1 = helper.make_node("Transpose", ["X"], ["Y"], perm=[0, 2, 3, 1], name="trans_1")
+        node2 = helper.make_node("DequantizeLinear", ["Y", "scale", "zero_point"], ["Z"], name="dequantize")
+        node3 = helper.make_node("Transpose", ["Z"], ["Z1"], perm=[0, 3, 1, 2], name="trans_2")
+
+        graph = helper.make_graph(
+            [node1, node2, node3],
+            "dequantize-test",
+            [helper.make_tensor_value_info("X", TensorProto.UINT8, (2, 3, 4, 5))],
+            [helper.make_tensor_value_info("Z1", TensorProto.FLOAT, (2, 3, 4, 5))],
+            [scale, zero_point]
+        )
+
+        model_proto = self.make_model(graph, producer_name="onnx-tests")
+        self.run_transpose_compare(["Z1"], {"X": np.random.randint(0, 100, (2, 3, 4, 5), np.uint8)},
+                                   model_proto, remaining_transpose_num=0)
+
+    @check_opset_min_version(13, "DequantizeLinear with axis")
+    def test_transpose_dequantize_with_axis(self):
+        scale = numpy_helper.from_array(np.array([0.75, 0.1, 2.3, 0.3, 0.42], dtype=np.float32), name='scale')
+        zero_point = numpy_helper.from_array(np.array([2, 4, 6, 8, 10], dtype=np.uint8), name='zero_point')
+        node1 = helper.make_node("Transpose", ["X"], ["Y"], perm=[0, 2, 3, 1], name="trans_1")
+        node2 = helper.make_node("DequantizeLinear", ["Y", "scale", "zero_point"], ["Z"], name="dequantize", axis=2)
+        node3 = helper.make_node("Transpose", ["Z"], ["Z1"], perm=[0, 3, 1, 2], name="trans_2")
+
+        graph = helper.make_graph(
+            [node1, node2, node3],
+            "dequantize-test",
+            [helper.make_tensor_value_info("X", TensorProto.UINT8, (2, 3, 4, 5))],
+            [helper.make_tensor_value_info("Z1", TensorProto.FLOAT, (2, 3, 4, 5))],
+            [scale, zero_point]
+        )
+
+        model_proto = self.make_model(graph, producer_name="onnx-tests")
+        self.run_transpose_compare(["Z1"], {"X": np.random.randint(0, 100, (2, 3, 4, 5), np.uint8)},
+                                   model_proto, remaining_transpose_num=0)
+
     @check_opset_min_version(10, "Slice in opset 10 can accept dymaic 'start' and 'ends'")
     def test_transpose_slice(self):
         starts = np.array([0, 0, 0, 0], dtype=np.int64)
diff --git a/tf2onnx/optimizer/transpose_optimizer.py b/tf2onnx/optimizer/transpose_optimizer.py
index 3b07b9355..5b11c4df0 100644
--- a/tf2onnx/optimizer/transpose_optimizer.py
+++ b/tf2onnx/optimizer/transpose_optimizer.py
@@ -202,6 +202,8 @@ def _initialize_handlers(self):
             "Sub": self._sub_handler,
             "Tanh": self._simple_through_handler,
             "Transpose": self._transpose_handler,
+            "DequantizeLinear": self._quantize_handler,
+            "QuantizeLinear": self._quantize_handler,
         }
 
     def _handle_node_having_branches(self, node):
@@ -698,6 +700,17 @@ def _slice_handler(self, trans, node):
                     return self._switch_transpose_and_node(node, trans)
         return False
 
+    def _quantize_handler(self, trans, node):
+        # Used for QuantizeLinear and DequantizeLinear
+        if not self._switch_transpose_and_node(node, trans):
+            return False
+        if 'axis' in node.attr:
+            perm = trans.get_attr_value("perm")
+            axis = node.get_attr_value("axis")
+            new_axis = perm[axis]
+            node.set_attr("axis", new_axis)
+        return True
+
     def _simple_through_handler(self, trans, node):
         return self._switch_transpose_and_node(node, trans)