Results.md

Results

Date: June 2, 2021.

Here, we give more explanation about the parameters that we used for our plots in Figure 8. (a) and (b) in the main paper.

In Figure 8. (a) and (b), we used MSE and MS-SSIM distortion functions for neural network-based model trainings, respectively.

For Balle-extended models, we scale both distortion functions by a factor of (255**2) in order to avoid choosing very small lambda values to operate in the range of 0-0.3 BPP region.

Note that we used window_size=7 in MS-SSIM distortion function as our training image size is of dimension 128x256.

We use the following lambda values to obtain both Figure 8. (a) and (b):

• ours+Ballé2017:
  • For MSEtrainings: [2e-05, 4e-05, 0.0002, 0.0005, 0.0008, 0.0012, 0.0014, 0.0016, 0.002, 0.0026, 0.0032, 0.0038, 0.0044, 0.005, 0.0056]
  • For MS-SSIM trainings: [1.2e-05, 3e-05, 4.5e-05, 6e-05, 8e-05, 9e-05, 0.0001, 0.00014, 0.0002, 0.0008, 0.0014, 0.005]
• ours+Ballé2018:
  • For MSE trainings: [0.0002, 0.0008, 0.0011, 0.0014, 0.002, 0.0026, 0.0032, 0.0038, 0.0044, 0.0048]
  • For MS-SSIM trainings: [1.2e-05, 1.6e-05, 3e-05, 4.5e-05, 6e-05, 0.0001, 0.00014, 0.00018, 0.0022]
• Ballé2018:
  • For MSE trainings: [0.0002, 0.0005, 0.0009, 0.001, 0.0011, 0.0014, 0.002, 0.0026, 0.0032, 0.0038, 0.0044, 0.0048]
  • For MS-SSIM trainings: [1.6e-05, 8e-05, 4e-06, 8e-06, 1.2e-05, 2e-05, 3e-05, 4.5e-05, 6e-05, 7e-05, 9e-05, 0.0001, 0.00014, 0.00018, 0.00022, 0.00026]
• Ballé2017:
  • For MSE trainings: [0.0002, 0.0004, 0.0008, 0.0014, 0.002, 0.0026, 0.0032, 0.0036, 0.0044, 0.0048]
  • For MS-SSIM trainings: [4e-06, 1.2e-05, 3e-05, 3.7e-05, 4.5e-05, 6e-05, 9e-05, 0.00012, 0.00014, 0.00016, 0.00018]

Note that not all lambda values indicated above are shown in Figure 8. (a) and (b).

To carry out JPEG 2000 and BPG evalutions, we used CompressAI library. We varied the quality metric, -q, in the following ranges for BPG and JPEG 2000:

• For BPG: [10, 11, 12, ... 51]
• For JPEG 2000: [20, 25, 30, 35, 40, 50, 75, 100, 125, ... 250]