Using AI to Upscale Video

With the endless scroll of social media and the click of a button providing instant access to information, it’s hard to tolerate low-quality content. In fact, according to Wyzowl, 91% of consumers feel that video quality impacts their trust in brands. With video viewership skyrocketing, businesses can’t afford to compromise on the quality of their video content if they want to succeed.

With the increasing demand for high-quality video content across various platforms, especially with the rise of 4K and 8K displays, several businesses adopt AI techniques to improve their video content delivery. In this article, we’ll explain what AI video upscaling is, how it enables businesses and individuals to thrive in today’s digital world, and provide best practices for optimizing your video management workflows.

In this article:

• Understanding How AI Video Upscaling Works
• Practical Applications of AI Video Upscaling
• How to Use AI to Upscale Videos

Understanding How AI Video Upscaling Works

Essentially, video upscaling is a term that refers to the action of improving the quality of a video footage. Using either traditional algorithms or the newer AI approaches, video upscaling improves image clarity and detail, resulting in a sharper, more refined picture.

Traditional upscaling methods, such as using video editing software, often result in blurry and pixelated output due to simple pixel interpolation. However, AI upscaling involves the use of advanced machine learning algorithms to enhance video resolution by analyzing each frame in a video and predicting missing details, ‌transforming low-quality videos into content that meets modern standards.

AI upscaling uses complex algorithms (for example, Super Resolution Generative Adversarial Network) powered by artificial intelligence to improve video resolution, sharpness, color accuracy, and visual fidelity, generating a more detailed and refined video.

A typical AI video upscaling process involves the following steps:

Training Deep Learning Models

There’s a popular saying that AI is only as good as the data, and many AI technologies obey this golden rule, garbage in, garbage out. At the heart of every deep learning model is an intrinsic dependence on data. This step trains deep learning models on vast datasets of high and low-resolution video clips and images.

Through this training process, the models learn to recognize patterns and features, such as clarity and sharpness, that distinguish high-quality video from low-quality video. Then, over time, the model learns to predict missing details in low-resolution video frames, enabling it to create and predict higher-quality pixels to upscale video clips.

Super-Resolution Techniques

Deconvolution and super resolution algorithms are common methods for correcting low-quality images and video footage. Super resolution simply means estimating a high resolution (HR) image or video from one or more low resolution (LR) observations.

In this step, super resolution algorithms for video processing, such as, Convolutional Neural Networks (CNNs) and Generative Adversarial Networks (GANs) enhance the resolution of low-resolution video frames by using both spatial information within a frame and temporal information across multiple frames, resulting in a higher resolution video output.

Frame Interpolation for Smoother Playback

Frame interpolation involves generating additional frames between existing ones to improve smoothness and reduce motion artifacts, especially for videos with lower frame rates. This is useful when upscaling old video clips or lower-quality digital video for streaming or playback on high-frame-rate devices.

Practical Applications of AI Video Upscaling

AI video upscaling is used in a wide range of fields and for many reasons, such as:

• Restoring and Enhancing Archive Footage: AI upscaling can make old, blurry, or low-resolution videos look sharp and modern. This is especially useful for old movies, documentaries, or important moments captured on tape before HD cameras became a thing. For example, a classic black-and-white movie can be digitally improved by adding color to make it look like it was filmed in high definition. This is also extremely valuable in broadcasting, film restoration, and digital archives that aim to preserve content for contemporary viewers.
• Improving Streaming Quality for Low-Resolution Content: Streaming services like Netflix and Amazon Prime Video use AI upscaling to enhance the quality of older, low-resolution videos without using more data, allowing viewers to enjoy a clearer picture–even if the original video isn’t high-quality. Additionally, AI upscaling allows these platforms to meet the demand for HD content without significantly increasing storage and processing costs.
• Enabling High-Quality Video for Content Creators: Most modern businesses use videos to engage with users and drive business growth across different platforms. AI upscaling allows businesses and content creators to improve older or low-quality videos so they can be reused for new purposes, like creating ads, social media posts, or other digital campaigns. This‌ reduces the need for re-recording or reshooting content, saving creators time and resources.
• Supporting Gaming and Virtual Reality (VR): AI is used in many different aspects of gaming and virtual reality to enhance users’ experience. AI video upscaling can make games look better by improving the textures and visual details in real-time, and for virtual reality, it can create highly realistic virtual environments, making the experience more lifelike and less superficial.
• Enhancing User-Generated Content (UGC): Several social media platforms and video-sharing websites like Vimeo and YouTube use AI video upscaling to automatically generate high-resolution versions of videos uploaded by their users. This creates a better viewing experience for viewers and helps creators serve.

How to Use AI to Upscale Videos

If you’re a developer looking for a programmatic way to optimize your videos, Cloudinary provides powerful tools and intelligent algorithms to upscale videos, all while maintaining high quality and optimizing performance for delivery.

Cloudinary provides a few ways to control the quality of videos delivered from Cloudinary. Let’s check them out:

Default Quality Setting

Here, you set a baseline quality for videos in your product environment from your Cloudinary dashboard. You can set this by logging in to your Cloudinary dashboard, then go to Settings > Optimization > Default video quality.

Automatic Quality Selection (q_auto)

This involves setting the quality transformation parameter to auto (q_auto in URLs) to let Cloudinary intelligently determine the optimal quality based on the user’s device and network conditions.There’s also support for controlling automatic quality selection using any of the following parameters: q_auto:best, q_auto:good, q_auto:eco, q_auto:low.

Manual Quality Control

This involves controlling the video quality with the quality parameter (q in URLs) to set the desired quality level for individual video deliveries. The quality value ranges between 1 (lowest) and 100 (highest).

cloudinary.video("video_public_id", {quality: 50})

WEBM Transcoding Quality

In some cases, transcoding videos from MP4 to WebM (VP8/9 encoding) may give a pixelated result. This transformation helps prevent over-quantization of the video by letting you set a maximum quantization percentage (1-100 range) value:qmax with a fixed quality value.

cloudinary.video("video_public_id", {transformation: [
  {duration: "30.0"},
  {quality: "70:qmax_20"}
  ]})

Bitrate Control

The bit_rate parameter controls the number of bits used to represent the video data. The parameter allows you to manage the amount of data transmitted per second to balance quality and performance. bit_rate can take one of the following values:

• An integer. For example, 120000.
• A string supporting ‘k’ and ‘m’ (kilobits and megabits respectively), such as 250k or 2m.

  cloudinary.video("video_public_id", {bit_rate: "250k"})

  You can learn more about optimizing video quality with Cloudinary in the docs.

  

  Conclusion

  AI video upscaling is transforming the way we experience and consume video content. By leveraging the power of deep learning, this technology enables us to restore and enhance archival footage, improve streaming quality, and elevate the visual experience for content creators and viewers alike.

  If you’re looking to optimize your video delivery processes, Cloudinary’s Video API and programmable SDKs provide a comprehensive solution for you to manage your videos, simplifying your workflows and delivering high-quality video content efficiently. Sign up for a free account to get started today!