diff --git a/.gitattributes b/.gitattributes
index f222e622962c1dd255a4154a289b8b782a1a167f..2de9fc99f732b23987b9df1ffd6e3d84e3a80360 100644
--- a/.gitattributes
+++ b/.gitattributes
@@ -127,3 +127,6 @@ assets/assets/models/lsk.glb filter=lfs diff=lfs merge=lfs -text
 assets/assets/models/outside.glb filter=lfs diff=lfs merge=lfs -text
 assets/t2v/couple.gif filter=lfs diff=lfs merge=lfs -text
 ECCV2022-RIFE-main/demo/I0_slomo_clipped.gif filter=lfs diff=lfs merge=lfs -text
+uvq/models/compressionnet_baseline/variables/variables.data-00000-of-00001 filter=lfs diff=lfs merge=lfs -text
+uvq/models/contentnet_baseline/variables/variables.data-00000-of-00001 filter=lfs diff=lfs merge=lfs -text
+uvq/models/distortionnet_baseline/variables/variables.data-00000-of-00001 filter=lfs diff=lfs merge=lfs -text
diff --git a/uvq/LICENSE b/uvq/LICENSE
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index 0000000000000000000000000000000000000000..d645695673349e3947e8e5ae42332d0ac3164cd7
--- /dev/null
+++ b/uvq/LICENSE
@@ -0,0 +1,202 @@
+
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diff --git a/uvq/README.md b/uvq/README.md
new file mode 100644
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+++ b/uvq/README.md
@@ -0,0 +1,99 @@
+# UVQ: Universal Video Quality Model 
+
+This repository contains checkpointed models of Google's Universal Video Quality (UVQ) model.
+UVQ is a no-reference perceptual video quality assessment model that is designed to work
+well on user-generated content, where there is no pristine reference.
+
+Read this blog post for an overview of UVQ:
+
+"[UVQ: Measuring YouTube's Perceptual Video Quality](https://ai.googleblog.com/2022/08/uvq-measuring-youtubes-perceptual-video.html)", Google AI Blog 2022
+
+More details are available in our paper:
+
+Yilin Wang, Junjie Ke, Hossein Talebi, Joong Gon Yim, Neil Birkbeck, Balu Adsumilli, Peyman Milanfar, Feng Yang, "[Rich features for perceptual quality assessment of UGC videos](https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Rich_Features_for_Perceptual_Quality_Assessment_of_UGC_Videos_CVPR_2021_paper.html)", CVPR 2021.
+
+The corresponding data from the paper is available for download from: [YouTube UGC Dataset](https://media.withyoutube.com)
+
+## Running the code
+
+### Dependencies
+
+You must have [FFmpeg](http://www.ffmpeg.org/) installed and available on your path.
+
+The models and code require Python 3.6 (or greater) and [Tensorflow](https://www.tensorflow.org/install).
+
+With virtualenv, you can install the requirements to a virtual environment:
+```
+virtualenv venv
+source venv/bin/activate
+pip3 install -r requirements.txt
+```
+
+### Predict Quality
+
+You can grab some examples videos from the [YouTube UGC Dataset](https://media.withyoutube.com). For example, you can get Gaming_1080P-0ce6_orig.mp4 using curl:
+
+```
+curl -o Gaming_1080P-0ce6_orig.mp4 https://storage.googleapis.com/ugc-dataset/vp9_compressed_videos/Gaming_1080P-0ce6_orig.mp4
+```
+
+You can then run the example:
+
+```bash
+mkdir -p results
+python3 uvq_main.py --input_files="Gaming_1080P-0ce6_orig,20,Gaming_1080P-0ce6_orig.mp4" --output_dir results --model_dir models
+```
+
+#### Input file formatting
+The input files format is a line with the following fields:
+
+`id,video_length,filepath`
+
+#### Results
+
+The `output_dir` will contain a csv file with the results for each model. For example,
+```bash
+cat results/mos_ytugc20s_0_Gaming_1080P-0ce6_orig.mp4_orig.csv
+```
+Gives:
+```bash
+Gaming_1080P-0ce6,compression,3.927867603302002
+Gaming_1080P-0ce6,content,3.945391607284546
+Gaming_1080P-0ce6,distortion,4.267196607589722
+Gaming_1080P-0ce6,compression_content,3.9505696296691895
+Gaming_1080P-0ce6,compression_distortion,4.062019920349121
+Gaming_1080P-0ce6,content_distortion,4.067790699005127
+Gaming_1080P-0ce6,compression_content_distortion,4.058663845062256
+```
+
+We provide multiple predcited scores, using different combinations of UVQ features.
+`compression_content_distortion` (combining three features) is our default score for Mean Opinion Score (MOS) prediction.
+
+The output features folder includes UVQ labels and raw features:
+```bash
+Gaming_1080P-0ce6_orig_feature_compression.binary
+Gaming_1080P-0ce6_orig_feature_content.binary
+Gaming_1080P-0ce6_orig_feature_distortion.binary
+Gaming_1080P-0ce6_orig_label_compression.csv
+Gaming_1080P-0ce6_orig_label_content.csv
+Gaming_1080P-0ce6_orig_label_distortion.csv
+```
+UVQ labels (.csv, each row corresponding to 1s chunk):<br />
+compression: 16 compression levels per row, corresponding to 4x4 subregions of the entire frame.<br />
+distortion: 26 distortion types defined in [KADID-10k](http://database.mmsp-kn.de/kadid-10k-database.html) for 2x2 subregions. The first element is the undefined type. <br /> 
+content: 3862 content labels defined in [YouTube-8M](https://research.google.com/youtube8m/).<br />
+
+UVQ raw features (in binary):<br />
+25600 float numbers per 1s chunk.
+
+
+## Contributors
+
+[//]: contributor-faces
+
+<a href="https://github.com/yilinwang01"><img src="https://avatars.githubusercontent.com/u/30224449?v=4" title="yilinwang01" width="80" height="80"></a>
+<a href="https://github.com/nbirkbeck"><img src="https://avatars.githubusercontent.com/u/6225937?v=4" title="nbirkbeck" width="80" height="80"></a>
+<a href="https://github.com/megabalu"><img src="https://avatars.githubusercontent.com/u/99928166?v=4" title="megabalu" width="80" height="80"></a>
+
+[//]: contributor-faces
+
diff --git a/uvq/__pycache__/uvq_utils.cpython-310.pyc b/uvq/__pycache__/uvq_utils.cpython-310.pyc
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diff --git a/uvq/docs/contributing.md b/uvq/docs/contributing.md
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@@ -0,0 +1,28 @@
+# How to Contribute
+
+We'd love to accept your patches and contributions to this project. There are
+just a few small guidelines you need to follow.
+
+## Contributor License Agreement
+
+Contributions to this project must be accompanied by a Contributor License
+Agreement. You (or your employer) retain the copyright to your contribution;
+this simply gives us permission to use and redistribute your contributions as
+part of the project. Head over to <https://cla.developers.google.com/> to see
+your current agreements on file or to sign a new one.
+
+You generally only need to submit a CLA once, so if you've already submitted one
+(even if it was for a different project), you probably don't need to do it
+again.
+
+## Code reviews
+
+All submissions, including submissions by project members, require review. We
+use GitHub pull requests for this purpose. Consult
+[GitHub Help](https://help.github.com/articles/about-pull-requests/) for more
+information on using pull requests.
+
+## Community Guidelines
+
+This project follows [Google's Open Source Community
+Guidelines](https://opensource.google/conduct/).
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diff --git a/uvq/models/aggregationnet_baseline/ytugc20s_4_avgpool_distortion/saved_model.pb b/uvq/models/aggregationnet_baseline/ytugc20s_4_avgpool_distortion/saved_model.pb
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@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
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+size 71477
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diff --git a/uvq/requirements.txt b/uvq/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..5bf8de1c4a8299978a5321b24bc3e36174614fe1
--- /dev/null
+++ b/uvq/requirements.txt
@@ -0,0 +1,3 @@
+numpy==1.23.1
+Pillow==9.2.0
+tensorflow==2.9.1
diff --git a/uvq/uvq_main.py b/uvq/uvq_main.py
new file mode 100644
index 0000000000000000000000000000000000000000..0dca183869c638020af3da71e78d2dfce3308a5a
--- /dev/null
+++ b/uvq/uvq_main.py
@@ -0,0 +1,60 @@
+"""Run inference with the UVQ model.
+
+Example usage:
+
+python3 uvq_main.py \
+  --input_files="Gaming_1080P-0ce6,20,Gaming_1080P-0ce6_orig.mp4" \
+  --output_dir=results \
+  --model_dir=models \
+  --transpose=False
+
+
+Copyright 2022 Google LLC
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    https://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+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.
+"""
+
+import tensorflow as tf
+
+import uvq_utils as utils
+from tensorflow.compat.v1 import flags, gfile
+
+
+FLAGS = tf.compat.v1.flags.FLAGS
+
+# Following parameters are required
+flags.DEFINE_string('input_files', '', 'configuration of input files.')
+flags.DEFINE_string('output_dir', '', 'Directory to save results.')
+flags.DEFINE_string('model_dir', 'models', 'Directory to UVQ models.')
+flags.DEFINE_bool('transpose', False, 'Whether to tranpose the input video.')
+
+
+def main(_):
+  # Input must be in format: video_id,video_length,file_path
+  video_id, video_length, filepath = FLAGS.input_files.split(',')
+  video_length = int(video_length)
+
+  output_dir = '%s/%s' % (FLAGS.output_dir, video_id)
+  feature_dir = '%s/features' % output_dir
+  if not gfile.IsDirectory(feature_dir):
+    gfile.MakeDirs(feature_dir)
+
+  utils.generate_features(video_id, video_length, filepath, FLAGS.model_dir,
+                          feature_dir, FLAGS.transpose)
+
+  utils.prediction(video_id, video_length, FLAGS.model_dir, feature_dir,
+                   output_dir)
+
+
+if __name__ == '__main__':
+  tf.compat.v1.app.run()
diff --git a/uvq/uvq_utils.py b/uvq/uvq_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..a83a65c170141c055bf31434b7b1dea803ae162e
--- /dev/null
+++ b/uvq/uvq_utils.py
@@ -0,0 +1,342 @@
+"""Utility functions of the UVQ model.
+
+Copyright 2022 Google LLC
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    https://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+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.
+"""
+
+
+import csv
+import os
+import subprocess
+import tempfile
+
+import numpy as np
+from PIL import Image
+import tensorflow as tf
+
+from tensorflow.compat.v1 import gfile, logging, saved_model
+from tensorflow.compat.v1 import ConfigProto, Session, Graph
+
+
+# Explicitly set input size
+VIDEO_HEIGHT = 720
+VIDEO_WIDTH = 1280
+VIDEO_FPS = 5
+VIDEO_CHANNEL = 3
+
+# Output feature size
+DIM_HEIGHT_FEATURE = 16
+DIM_WIDTH_FEATURE = 16
+DIM_CHANNEL_FEATURE = 100
+
+# ContentNet specs
+INPUT_HEIGHT_CONTENT = 496
+INPUT_WIDTH_CONTENT = 496
+INPUT_CHANNEL_CONTENT = 3
+DIM_LABEL_CONTENT = 3862
+
+def extend_array(rgb, total_len):
+  """Extends the byte array (or truncates) to be total_len"""
+  missing = total_len - len(rgb)
+  if missing < 0:
+    rgb = rgb[0 : total_len]
+  else:
+    rgb.extend(bytearray(missing))
+  return rgb
+
+
+def load_video(filepath, video_length, transpose=False):
+  """Load input video."""
+  # Rotate video if requested
+  if transpose:
+    transpose_param = 'transpose=1,'
+  else:
+    transpose_param = ''
+
+  # Sample at constant frame rate, and save as RGB24 (RGBRGB...)
+  fd, temp_filename = tempfile.mkstemp()
+  fd_small, temp_filename_small = tempfile.mkstemp()
+  cmd = (
+      'ffmpeg  -i %s -filter_complex '
+      ' "[0:v]%sscale=w=%d:h=%d:flags=bicubic:force_original_aspect_ratio=1,'
+      'pad=%d:%d:(ow-iw)/2:(oh-ih)/2,format=rgb24,split=2[out1][tmp],[tmp]scale=%d:%d:flags=bilinear[out2]"'
+      ' -map [out1] -r %d -f rawvideo -pix_fmt rgb24 -y %s'
+      ' -map [out2] -r %d -f rawvideo -pix_fmt rgb24 -y %s'
+  ) % (filepath, transpose_param, VIDEO_WIDTH, VIDEO_HEIGHT,
+       VIDEO_WIDTH, VIDEO_HEIGHT, INPUT_WIDTH_CONTENT, INPUT_HEIGHT_CONTENT,
+       VIDEO_FPS, temp_filename, VIDEO_FPS, temp_filename_small)
+
+  try:
+    logging.info('Run with cmd:% s\n', cmd)
+    subprocess.check_output(cmd, stderr=subprocess.STDOUT, shell=True)
+  except subprocess.CalledProcessError as error:
+    logging.fatal('Run with cmd: %s \n terminated with return code %s\n%s',
+                  cmd, str(error.returncode), error.output)
+    raise error
+
+  # For video, the entire video is divided into 1s chunks in 5 fps
+  with gfile.Open(temp_filename, 'rb') as rgb_file, gfile.Open(temp_filename_small, 'rb') as rgb_file_small:
+    single_frame_size = VIDEO_WIDTH * VIDEO_HEIGHT * VIDEO_CHANNEL
+    full_decode_size = video_length * VIDEO_FPS * single_frame_size
+    assert rgb_file.size() >= single_frame_size,  f'Decoding failed to output a single frame: {rgb_file.size()} < {single_frame_size}'
+    if rgb_file.size() < full_decode_size:
+      logging.warn('Decoding may be truncated: %d bytes (%d frames) < %d bytes (%d frames),'
+                   ' or video length (%ds) may be too incorrect',
+                   rgb_file.size(), rgb_file.size() / single_frame_size,
+                   full_decode_size, full_decode_size / single_frame_size,
+                   video_length)
+
+    rgb = extend_array(bytearray(rgb_file.read()), full_decode_size)
+    rgb_small = extend_array(bytearray(rgb_file_small.read()),
+                             video_length * VIDEO_FPS * INPUT_WIDTH_CONTENT * INPUT_HEIGHT_CONTENT * VIDEO_CHANNEL)
+    video = (np.reshape(np.frombuffer(rgb, 'uint8'),
+                        (video_length, int(VIDEO_FPS), VIDEO_HEIGHT, VIDEO_WIDTH, 3))/255.0 - 0.5) *2
+    video_resized = (np.reshape(np.frombuffer(rgb_small, 'uint8'),
+                                (video_length, int(VIDEO_FPS), INPUT_HEIGHT_CONTENT, INPUT_WIDTH_CONTENT, 3))/255.0 - 0.5) *2
+
+  # Delete temp files
+  os.close(fd)
+  os.remove(temp_filename)
+  os.close(fd_small)
+  os.remove(temp_filename_small)
+  logging.info('Load %s done successfully.', filepath)
+  return video, video_resized
+
+
+def generate_content_feature(video, model_path, input_node, output_nodes):
+  """Extract features from ContentNet."""
+  with Session(graph=Graph(), config=ConfigProto(
+      allow_soft_placement=True, log_device_placement=False)) as sess:
+    saved_model.loader.load(
+        sess, [saved_model.tag_constants.SERVING], model_path)
+
+    label = np.ndarray((video.shape[0], DIM_LABEL_CONTENT), np.float32)
+    feature = np.ndarray((video.shape[0], DIM_HEIGHT_FEATURE,
+                          DIM_WIDTH_FEATURE, DIM_CHANNEL_FEATURE), np.float32)
+
+    patch = np.ndarray((1, INPUT_HEIGHT_CONTENT, INPUT_WIDTH_CONTENT,
+                        INPUT_CHANNEL_CONTENT), np.float32)
+
+    for k in range(video.shape[0]):
+      patch[0, :] = video[k, 0, :, :, :]
+      patch_feature, patch_label = sess.run(output_nodes,
+                                            feed_dict={input_node: patch})
+
+      feature[k, :, :, :] = patch_feature
+      label[k, :] = patch_label
+    return feature, label
+
+
+def generate_subnet_feature(video, model_path, input_width, input_height,
+                            input_fps, feature_width, feature_height,
+                            feature_channel, label_dim, input_node,
+                            output_nodes):
+  """Extract features from CompresionNet or DistortionNet."""
+  with Session(graph=Graph(), config=ConfigProto(
+      allow_soft_placement=True, log_device_placement=False)) as sess:
+    saved_model.loader.load(
+        sess, [saved_model.tag_constants.SERVING], model_path)
+
+    num_h = int(VIDEO_HEIGHT / input_height)
+    num_w = int(VIDEO_WIDTH / input_width)
+
+    label = np.ndarray((video.shape[0], num_h, num_w, label_dim), np.float32)
+    feature = np.ndarray((video.shape[0], num_h * feature_height,
+                          num_w * feature_width, feature_channel), np.float32)
+
+    if input_fps == 1:
+      patch = np.ndarray((1, input_height, input_width,
+                          video.shape[-1]), np.float32)
+    else:
+      patch = np.ndarray((1, input_fps, input_height, input_width,
+                          video.shape[-1]), np.float32)
+
+    for k in range(video.shape[0]):
+      for j in range(num_h):
+        for i in range(num_w):
+          if input_fps == 1:
+            patch[0, :] = video[k, 0, j * input_height:(j + 1) * input_height,
+                                i * input_width:(i + 1) * input_width, :]
+          else:
+            patch[0, :] = video[k, :, j * input_height:(j + 1) * input_height,
+                                i * input_width:(i + 1) * input_width, :]
+
+          patch_feature, patch_label = sess.run(output_nodes,
+                                                feed_dict={input_node: patch})
+
+          feature[k, j * feature_height:(j + 1) * feature_height, i *
+                  feature_width:(i + 1) * feature_width, :] = patch_feature
+
+          label[k, j, i, :] = patch_label
+
+    return feature, label
+
+
+def generate_features(video_id, video_length, filepath, model_dir, output_dir,
+                      transpose=False):
+  """Generate features from input video."""
+  video, video_resized = load_video(filepath, video_length, transpose)
+
+  feature_compression, label_compression = generate_subnet_feature(
+      video, '%s/compressionnet_baseline' % model_dir,
+      320, 180, 5,  # input height, weight, fps,
+      4, 4, 100, 1,  # feature map height, width, channels, and label_size
+      'input_orig:0',
+      ['feature_layer_orig:0', 'compress_level_orig:0'])
+
+  feature_content, label_content = generate_content_feature(
+      video_resized, '%s/contentnet_baseline' % model_dir,
+      'map/TensorArrayV2Stack/TensorListStack:0',
+      ['final_conv2d/Conv2D:0', 'class_confidence:0'])
+
+  feature_distortion, label_distortion = generate_subnet_feature(
+      video, '%s/distortionnet_baseline' % model_dir,
+      640, 360, 1,  # input height, weight, fps,
+      8, 8, 100, 26,  # feature map height, width, channels, and label_size
+      'input_images:0',
+      ['feature_map:0', 'dist_type_prediction/dist_type_predictions:0'])
+
+  # Save features
+  fd, temp = tempfile.mkstemp()
+
+  feature_compression.astype('float32').tofile(temp)
+  out_feature = '%s/%s_feature_compression.binary' % (output_dir, video_id)
+  gfile.Copy(temp, out_feature, overwrite=True)
+
+  feature_content.astype('float32').tofile(temp)
+  out_feature = '%s/%s_feature_content.binary' % (output_dir, video_id)
+  gfile.Copy(temp, out_feature, overwrite=True)
+
+  feature_distortion.astype('float32').tofile(temp)
+  out_feature = '%s/%s_feature_distortion.binary' % (output_dir, video_id)
+  gfile.Copy(temp, out_feature, overwrite=True)
+
+  # Feature labels
+  np.savetxt(temp, label_compression.reshape(label_compression.shape[0], -1),
+             fmt='%0.3f', delimiter=',')
+  out_feature = '%s/%s_label_compression.csv' % (output_dir, video_id)
+  gfile.Copy(temp, out_feature, overwrite=True)
+
+  np.savetxt(temp, label_content.reshape(label_content.shape[0], -1),
+             fmt='%0.3f', delimiter=',')
+  out_feature = '%s/%s_label_content.csv' % (output_dir, video_id)
+  gfile.Copy(temp, out_feature, overwrite=True)
+
+  np.savetxt(temp, label_distortion.reshape(label_distortion.shape[0], -1),
+             fmt='%0.3f', delimiter=',')
+  out_feature = '%s/%s_label_distortion.csv' % (output_dir, video_id)
+  gfile.Copy(temp, out_feature, overwrite=True)
+
+  os.close(fd)
+  os.remove(temp)
+
+
+def load_features(video_id, dim_time, feature_dir):
+  """Load pre-generated features."""
+  input_compression_feature = '%s/%s_feature_compression.binary' % (
+      feature_dir, video_id)
+  with gfile.Open(input_compression_feature, 'rb') as input_file:
+    s = input_file.read()
+    with Session() as sess:
+      feature_1d = tf.io.decode_raw(s, out_type=tf.float32)
+
+      feature = tf.reshape(feature_1d,
+                           [1, dim_time, DIM_HEIGHT_FEATURE,
+                            DIM_WIDTH_FEATURE, DIM_CHANNEL_FEATURE])
+      feature_compression = sess.run(feature)
+
+  input_content_feature = '%s/%s_feature_content.binary' % (
+      feature_dir, video_id)
+  with gfile.Open(input_content_feature, 'rb') as input_file:
+    s = input_file.read()
+    with Session() as sess:
+      feature_1d = tf.io.decode_raw(s, out_type=tf.float32)
+
+      feature = tf.reshape(feature_1d,
+                           [1, dim_time, DIM_HEIGHT_FEATURE,
+                            DIM_WIDTH_FEATURE, DIM_CHANNEL_FEATURE])
+      feature_content = sess.run(feature)
+
+  input_distortion_feature = '%s/%s_feature_distortion.binary' % (
+      feature_dir, video_id)
+  with gfile.Open(input_distortion_feature, 'rb') as input_file:
+    s = input_file.read()
+    with Session() as sess:
+      feature_1d = tf.io.decode_raw(s, out_type=tf.float32)
+
+      feature = tf.reshape(feature_1d,
+                           [1, dim_time, DIM_HEIGHT_FEATURE,
+                            DIM_WIDTH_FEATURE, DIM_CHANNEL_FEATURE])
+      feature_distortion = sess.run(feature)
+
+  return feature_compression, feature_content, feature_distortion
+
+
+def prediction(video_id, video_length, model_dir, feature_dir, output_dir):
+  """Predict quality (MOS)."""
+  trainset = 'ytugc20s'
+  all_trainset_subindex = ['0', '1', '2', '3', '4']
+
+  all_feature = ['compression',
+                 'content',
+                 'distortion',
+                 'compression_content',
+                 'compression_distortion',
+                 'content_distortion',
+                 'compression_content_distortion',
+                 ]
+  aggregation_model = 'avgpool'
+  all_outputs = []
+  for feature in all_feature:
+    aggregated_mos = 0
+    for trainset_subindex in all_trainset_subindex:
+      model_name = '%s_%s_%s_%s' % (
+          trainset, trainset_subindex, aggregation_model, feature)
+
+      with Session(graph=Graph(), config=ConfigProto(
+          allow_soft_placement=True, log_device_placement=False)) as sess:
+        saved_model.loader.load(
+            sess, [saved_model.tag_constants.SERVING],
+            '%s/aggregationnet_baseline/%s' % (
+                model_dir, model_name))
+
+        [feature_compression, feature_content, feature_distortion
+         ] = load_features(video_id, video_length, feature_dir)
+        feature_compression = feature_compression[
+            :, 0:video_length, :, :, :]
+        feature_content = feature_content[
+            :, 0:video_length, :, :, :]
+        feature_distortion = feature_distortion[
+            :, 0:video_length, :, :, :]
+
+        pred_mos = sess.run(
+            'Model/mos:0',
+            feed_dict={'feature_compression:0': feature_compression,
+                       'feature_content:0': feature_content,
+                       'feature_distortion:0': feature_distortion,
+                       })
+        pred_mos = pred_mos[0][0]
+        aggregated_mos += pred_mos
+
+    aggregated_mos /= len(all_trainset_subindex)
+    all_outputs.append([video_id, feature, aggregated_mos])
+
+    fd, temp = tempfile.mkstemp()
+    with gfile.Open(temp, 'w') as f:
+      writer = csv.writer(f)
+      writer.writerows(all_outputs)
+    out_file = '%s/%s_uvq.csv' % (output_dir, video_id)
+    gfile.Copy(temp, out_file, overwrite=True)
+    os.close(fd)
+    os.remove(temp)