Dr. Erica Walker has a diverse background in visual communications which includes feature film production, web design & development, print buying, marketing, and graphic design for print. As a faculty member in the Department of Graphic Communications in the College of Business at Clemson University, Walker teaches courses in Photography, Video, Web Development, and Entrepreneurship. Walker is interested in many areas of research including software application, curriculum development, the entrepreneurial mindset, and artificial intelligence applications in color management for brands.

Color Correction In Video: 
Determining the Best Combination of Equipment 
and Settings to Capture Brand Color

Masy Golabkesh and Dr. Erica Walker, Clemson University

Color is an essential tool that delivers a brand image with more substantial persuasive power than shape, and it appeals to emotions rather than rationality. Color is also closely related to consumer behavior. As a result, color plays an influential communication role in situations where direct and specific verbal expression is complex. Moreover, color serves as a visual language that provides information to the sensory mode – sight – with the highest information capacity of the five human senses (Rubio, Oubiña, Gómez-Suárez, 2015).

In live sports event broadcasting, the team color can look different from the intended color when displayed on the screen. Accurately recreating the team brand color is very important. Companies spend a lot of money on developing and protecting their brand color pallet. They choose specific colors for their brand to identify between crowded marketplaces, and they want to see accurate brand colors portrayed in live events. Sport team fans recognize their team color and expect to see the consistent color in different games (Mayes, Lineberger, Mayer, Sanborn, Smith & Walker, 2021). Displaying the wrong color on screens may confuse fans.

Multiple variables play a role in capturing and displaying colors, such as camera body, lens, camera settings, ambient light, and camera positions to the field and lights. Current systems of ensuring the accuracy of the brand color of sports teams onscreen require manual adjustments of the camera footage in real-time or in post-production. To reduce this manual effort, we need to understand the variables that impact the colors.

In this study, we test the camera-level capture of Clemson orange by changing multiple variables. We have examined the following variables in this experiment: five camera models, six lenses, five camera positions with the player, two sets of camera Picture Profile settings, and two types of lighting (inside lighting using LED lights and outside lighting with ambient light). This resulted in 600 unique samples. These camera, settings, and lens combinations were chosen to mimic the on-field situations encountered by content creators for Clemson Athletics.

We shot 4-second clips of the still life which included a helmet, a jersey, a Pantone book open to uncoated Pantone 152 U and coated Pantone 1595 C, and an X-Rite Color Checker for reference with the different combinations of camera bodies, lenses, positions, and lighting. Using a program written in Python leveraging the strengths of Artificial Intelligence and previous work in this area on ColorNet (Walker, Smith, Mayes, Lineberger, Mayer & Sanborne, 2020), we sampled frames from the footage to create the data set. In the next step, we measure pixels from each frame. To increase the pixel sampling randomness, we extract multiple pixels in different areas such as shadow, mid-tone, and highlight areas of the frame. Then we calculated the RGB values for each pixel and averaged the RGBs values for each clip.

Then we compared the produced RGB values with Clemson brand orange RGB values in the different permutations to better understand the variables that impact brand color capture on video and determine the ideal settings and situations to reproduce brand color accurately at the camera level. We used the T-test to compare produced RGB values with Clemson Brand orange RGB values and determined the difference between the two values. Knowing to use the proper parameter settings during camera capture for broadcast will help to reduce the timely post-production process and create accurate brand colors in real-time.


Rubio, N., Oubiña, J., & Gómez-Suárez, M. (2015). Understanding brand loyalty of the store brand’s customer base. The Journal of Product & Brand Management, 24(7), 679–692. https://doi.org/10.1108/JPBM-03-2015-0822

Mayes, E., Lineberger, J.P., Mayer, M., Sanborne, A., Smith, D.H., & Walker, E.B., “Automated Brand Color Accuracy for Real-Time Video.” SMPTE Motion Imaging Journal. https://ieeexplore.ieee.org/document/9395672

Walker, E.B., Smith, D.H., Mayes, E., Lineberger, J.P., Mayer, M., & Sanborne, A., “Consistent display of Clemson brand colors using artificial intelligence.” Technical Association of the Graphic Arts (TAGA), June 2020, Oklahoma City, OK.

ColorNet 2.0: Image Segmentation for 
Brand Color Correction in Video

Michelle Mayer, Dr. Erica Walker, Jake Liguori, and Hudson Smith, Clemson University

A commonly used technique in image processing is image segmentation, partitioning an image into multiple parts or regions based on the characteristics of pixels in the image. For example, image segmentation could involve separating foreground from background, or grouping pixels based on the presence of objects or on similar textures, colors, or brightness (What Is Image Segmentation? 3 things you need to know, n.d.). Segmentation can be defined as a pixel-level classification, where instead of labeling a whole image as a certain object, each individual pixel is labeled or classified into a distinct category, Figure 1.

Figure 1. An example segmentation where pixels labeled as vehicles are colored red, buildings are yellow, etc. (Palac, B, 2020).

Applications of image segmentation are widespread. Medical professionals use segmentation for highly accurate labeling of medical scans (Havaei, M. et al., 2017). Each pixel in the image that corresponds to a tumor can be labeled with a different color to help the doctor better identify the tumor’s exact shape. Segmentation is also an important component of autonomous vehicle systems. Numerous cameras and other sensors continually collect data that self-driving cars use to make decisions (Xu, H. et al., 2017). It is extremely important that the machine learning model be highly accurate and have low latency to detect, identify, and respond correctly to objects that appear in the video feed whether they are road markings, detour signs, or pedestrians.

The ColorNet research team at Clemson University has recently developed a novel application of segmentation: color correcting live video feeds (Mayes, E. et al., 2020). One of the most significant issues with the current approach to color management in sports broadcasting is that color adjustments made to the frame impact all pixels. As a result, adjusting the RGB values to make a team’s jerseys the appropriate brand color specification can negatively impact players’ skin tones or the opposing team’s brand colors (Walker, E.B. et al., 2020). The ideal solution would allow the technician to select certain regions of pixels on the screen for color adjustment.

ColorNet 2.0 is a segmentation model that allows the user to input a target color and segment out portions of the screen containing pixels similar to the target color, Figure 2. There is no specific object to segment, but instead a chosen part of the color spectrum. During implementation, the model receives an uncorrected image and a target color then assigns each pixel in the original image a probability of being the brand color. The probabilities are then thresholded, and the network outputs a pixel mask that labels which pixels in the input frame correspond to the target brand color. Then, the technician can adjust the color of the targeted pixels without impacting any surrounding colors.

Figure 2. Segmentation masks for selecting Clemson orange (middle) and Clemson purple (right).

The ColorNet 2.0 model builds upon ColorNet 1.5 which successfully used regression to predict the correct RGB values of pixels and perform automatic color correction on sequences of video frames. ColorNet 1.5 demonstrated the correction of Clemson Orange and Clemson Purple, but the model requires more training data to correct for each additional color. In addition, the technician did not have the opportunity to manually tweak the corrections with this version. ColorNet 2.0 fixes these limitations by introducing segmentation to create a model that can correct any specified team color and integration for technicians to target and manually adjust brand colors during broadcast. Augmentation during model training synthetically expands the diversity of the training dataset to include all ACC team colors, Figure 3.

Figure 3. Augmentation results from shifting Clemson orange to appear blue.

To correct an image for multiple colors, one frame can be passed through the model several times, each time with a different target color. Therefore, it is no longer necessary to create separate models for each color and multiple colors can be corrected to different targets simultaneously. This manuscript provides an overview of the ColorNet 2.0 neural network architecture, model evaluation metrics, and performance.


Havaei, M., Davy, A., Warde-Farley, D. , Biard, A., Courville, A., Bengio, Y., Pal, C., Jodoin, P., & Larochelle, H. (2017). Brain tumor segmentation with Deep Neural Networks. Medical Image Analysis (Vol. 35). Retrieved October 18, 2021 from https://www.sciencedirect.com/science/article/abs/pii/S1361841516300330?via%3Dihub

Mayes, E., Lineberger, J.P., Mayer, M., Sanborn, A., Smith, H.D., & Walker, E.B. (2020). Automated Brand Color Accuracy for Real Time Video. Proceedings of the 2020 NAB Broadcast Engineering and Information Technology Conference. NAB Broadcast Engineering and Information Technology (BEIT) Conference, Las Vegas, NV. Retrieved October 18, 2021, from https://nabpilot.org/beitc-proceedings/2020/automated-brand-color-accuracy-for-real-time-video/

Palac, B. (2020). CC BY-SA 4.0 https://creativecommons.org/licenses/by-sa/4.0. Retrieved October 18, 2021 from https://commons.wikimedia.org/wiki/File:Image_segmentation.png

Walker, E.B., Smith, H.D., Mayes, E., Lineberger, J.P., Mayer, M., & Sanborn, A. (2020). Consistent Display of Clemson Brand Colors Using Artificial Intelligence. Technical Association of the Graphic Arts (TAGA) Conference, June 2020, Oklahoma City, OK. Retrieved October 18, 2021

What Is Image Segmentation? 3 things you need to know. (n.d.). Retrieved October 18, 2021 from https://www.mathworks.com/discovery/image-segmentation.html

Xu, H., Gao, Y., Yu, F., & Darrell, T. (2017). End-to-end Learning of Driving Models from Large-scale Video Datasets. Computer Vision Foundation. Retrieved October 18, 2021 from https://openaccess.thecvf.com/content_cvpr_2017/papers/Xu_End-To-End_Learning_of_CVPR_2017_paper.pdf