diff --git a/src/movie_barcodes/barcode_generation.py b/src/movie_barcodes/barcode_generation.py
index 8fede55..33539e2 100644
--- a/src/movie_barcodes/barcode_generation.py
+++ b/src/movie_barcodes/barcode_generation.py
@@ -35,13 +35,13 @@ def generate_circular_barcode(colors: list, img_size: int, scale_factor: int = 1
     ):
         radius = (idx + 1) * radius_increment
 
-        # Handle both simple RGB tuples and smoothed frames
+        # Handle both simple BGR tuples and smoothed frames
         if isinstance(color, np.ndarray) and color.ndim > 1:
-            # For smoothed frames, take the average color
+            # For smoothed frames, take the average BGR color
             color = np.mean(color, axis=(0, 1)).astype(int)
 
-        # Ensure color is in BGR format for OpenCV
-        bgr_color = color[::-1] if len(color) == 3 else color[2::-1]
+        # Colors are BGR throughout the pipeline; draw directly in BGR
+        bgr_color = color
 
         cv2.circle(
             barcode_high_res,
@@ -89,8 +89,7 @@ def generate_barcode(
                 # For single color values
                 color_column = np.full((frame_height, 1, 3), color, dtype=np.uint8)
 
-            # Convert to BGR (which will be interpreted as RGB when saved with PIL)
-            bgr_color = cv2.cvtColor(color_column, cv2.COLOR_RGB2BGR)
-            barcode[:, i] = bgr_color.reshape(frame_height, 3)
+            # Keep BGR internally; perform RGB conversion once at save-time
+            barcode[:, i] = color_column.reshape(frame_height, 3)
 
     return barcode
diff --git a/src/movie_barcodes/utility.py b/src/movie_barcodes/utility.py
index 75c911d..f591e0c 100644
--- a/src/movie_barcodes/utility.py
+++ b/src/movie_barcodes/utility.py
@@ -160,9 +160,13 @@ def save_barcode_image(barcode: np.ndarray, base_name: str, args: argparse.Names
             destination_path = path.join(project_root, destination_path)
 
     if barcode.shape[2] == 4:  # If the image has an alpha channel (RGBA)
-        image = Image.fromarray(barcode, "RGBA")
+        # Convert BGRA -> RGBA once at save-time
+        barcode_to_save = cv2.cvtColor(barcode, cv2.COLOR_BGRA2RGBA)
+        image = Image.fromarray(barcode_to_save, "RGBA")
     else:  # If the image doesn't have an alpha channel (RGB)
-        image = Image.fromarray(barcode, "RGB")
+        # Convert BGR -> RGB once at save-time
+        barcode_to_save = cv2.cvtColor(barcode, cv2.COLOR_BGR2RGB)
+        image = Image.fromarray(barcode_to_save, "RGB")
 
     image.save(destination_path)
     logging.info("File saved at '%s'", destination_path)
diff --git a/src/movie_barcodes/video_processing.py b/src/movie_barcodes/video_processing.py
index 2c7b459..da0d5f4 100644
--- a/src/movie_barcodes/video_processing.py
+++ b/src/movie_barcodes/video_processing.py
@@ -62,6 +62,7 @@ def parallel_extract_colors(
                 (i + 1) * frames_per_worker - 1,
                 color_extractor,
                 target_frames_per_worker,
+                False,  # disable worker progress bars
             )
             for i in range(workers)
         ]
@@ -73,6 +74,7 @@ def parallel_extract_colors(
                 frame_count - 1,
                 color_extractor,
                 target_frames - (workers - 1) * target_frames_per_worker,
+                False,
             )
 
         results = pool.starmap(extract_colors, args)
@@ -89,6 +91,7 @@ def extract_colors(
     end_frame: int,
     color_extractor: Callable,
     target_frames: Optional[int] = None,
+    show_progress: bool = True,
 ) -> List:
     """
     Extracts dominant colors from frames in a video file.
@@ -98,6 +101,7 @@ def extract_colors(
     :param int end_frame: The index of the last frame to process.
     :param Callable color_extractor: A function to extract the dominant color from a frame.
     :param Optional[int] target_frames: The total number of frames to sample.
+    :param bool show_progress: Whether to display a tqdm progress bar during extraction.
     :return: List of dominant colors from the sampled frames.
     """
     video = cv2.VideoCapture(video_path)
@@ -112,7 +116,11 @@ def extract_colors(
 
     colors = []
 
-    for _ in tqdm(range(target_frames or total_frames), desc="Processing frames"):
+    iterator = range(target_frames or total_frames)
+    if show_progress:
+        iterator = tqdm(iterator, desc="Processing frames")
+
+    for _ in iterator:
         ret, frame = video.read()  # Read the first or next frame
         if ret:
             dominant_color = color_extractor(frame)