Monthly Archives: June 2021

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Posted by Leopold Haller and Hernan Moraldo, Software Engineers, Google Research

In the last two decades, dramatic advances in compute and connectivity have allowed game developers to create works of ever-increasing scope and complexity. Simple linear levels have evolved into photorealistic open worlds, procedural algorithms have enabled games with unprecedented variety, and expanding internet access has transformed games into dynamic online services. Unfortunately, scope and complexity have grown more rapidly than the size of quality assurance teams or the capabilities of traditional automated testing. This poses a challenge to both product quality (such as delayed releases and post-launch patches) and developer quality of life.

Machine learning (ML) techniques offer a possible solution, as they have demonstrated the potential to profoundly impact game development flows — they can help designers balance their game and empower artists to produce high-quality assets in a fraction of the time traditionally required. Furthermore, they can be used to train challenging opponents that can compete at the highest levels of play. Yet some ML techniques can pose requirements that currently make them impractical for production game teams, including the design of game-specific network architectures, the development of expertise in implementing ML algorithms, or the generation of billions of frames of training data. Conversely, game developers operate in a setting that offers unique advantages to leverage ML techniques, such as direct access to the game source, an abundance of expert demonstrations, and the uniquely interactive nature of video games.

Today, we present an ML-based system that game developers can use to quickly and efficiently train game-testing agents, helping developers find serious bugs quickly, while allowing human testers to focus on more complex and intricate problems. The resulting solution requires no ML expertise, works on many of the most popular game genres, and can train an ML policy, which generates game actions from the game state, in less than an hour on a single game instance. We have also released an open source library that demonstrates a functional application of these techniques.

Supported genres include arcade, action/adventure, and racing games.

The Right Tool for the Right Job
The most elemental form of video game testing is to simply play the game. A lot. Many of the most serious bugs (such as crashes or falling out of the world) are easy to detect and fix; the challenge is finding them within the vast state space of a modern game. As such, we decided to focus on training a system that could “just play the game” at scale.

We found that the most effective way to do this was not to try to train a single, super-effective agent that could play the entire game from end-to-end, but to provide developers with the ability to train an ensemble of game-testing agents, each of which could effectively accomplish tasks of a few minutes each, which game developers refer to as “gameplay loops”.

These core gameplay behaviors are often expensive to program through traditional means, but are much more efficient to train than a single end-to-end ML model. In practice, commercial games create longer loops by repeating and remixing core gameplay loops, which means that developers can test large stretches of gameplay by combining ML policies with a small amount of simple scripting.

Simulation-centric, Semantic API
One of the most fundamental challenges in applying ML to game development is bridging the chasm between the simulation-centric world of video games and the data-centric world of ML. Rather than ask developers to directly convert the game state into custom, low-level ML features (which would be too labor intensive) or attempting to learn from raw pixels (which would require too much data to train), our system provides developers with an idiomatic, game-developer friendly API that allows them to describe their game in terms of the essential state that a player observes and the semantic actions they can perform. All of this information is expressed via concepts that are familiar to game developers, such as entities, raycasts, 3D positions and rotations, buttons and joysticks.

As you can see in the example below, the API allows the specification of observations and actions in just a few lines of code.

Example actions and observations for a racing game.

From API to Neural Network
This high level, semantic API is not just easy to use, but also allows the system to flexibly adapt to the specific game being developed — the specific combination of API building blocks employed by the game developer informs our choice of network architecture, since it provides information about the type of gaming scenario in which the system is deployed. Some examples of this include: handling action outputs differently depending on whether they represent a digital button or analog joystick, or using techniques from image processing to handle observations that result from an agent probing its environment with raycasts (similar to how autonomous vehicles probe their environment with LIDAR).

Our API is sufficiently general to allow modeling of many common control-schemes (the configuration of action outputs that control movement) in games, such as first-person games, third-person games with camera-relative controls, racing games, twin stick shooters, etc. Since 3D movement and aiming are often an integral aspect of gameplay in general, we create networks that automatically tend towards simple behaviors such as aiming, approach or avoidance in these games. The system accomplishes this by analyzing the game’s control scheme to create neural network layers that perform custom processing of observations and actions in that game. For example, positions and rotations of objects in the world are automatically translated into directions and distances from the point of view of the AI-controlled game entity. This transformation typically increases the speed of learning and helps the learned network generalize better.

An example neural network generated for a game with joystick controls and raycast inputs. Depending on the inputs (red) and the control scheme, the system generates custom pre- and post-processing layers (orange).

Learning From The Experts in Real Time
After generating a neural network architecture, the network needs to be trained to play the game using an appropriate choice of learning algorithm.

Reinforcement learning (RL), in which an ML policy is trained directly to maximize a reward, may seem like the obvious choice since they have been successfully used to train highly competent ML policies for games. However, RL algorithms tend to require more data than a single game instance can produce in a reasonable amount of time, and achieving good results in a new domain often requires hyperparameter tuning and strong ML domain knowledge.

Instead, we found that imitation learning (IL), which trains ML policies based by observing experts play the game, works well for our use case. Unlike RL, where the agent needs to discover a good policy on its own, IL only needs to recreate the behavior of a human expert. Since game developers and testers are experts in their own games, they can easily provide demonstrations of how to play the game.

We use an IL approach inspired by the DAgger algorithm, which allows us to take advantage of video games’ most compelling quality — interactivity. Thanks to the reductions in training time and data requirements enabled by our semantic API, training is effectively realtime, giving a developer the ability to fluidly switch between providing gameplay demonstrations and watching the system play. This results in a natural feedback loop, in which a developer iteratively provides corrections to a continuous stream of ML policies.

From the developer’s perspective, providing a demonstration or a correction to faulty behavior is as simple as picking up the controller and starting to play the game. Once they are done, they can put the controller down and watch the ML policy play. The result is a training experience that is real-time, interactive, highly experiential, and, very often, more than a little fun.

ML policy for an FPS game, trained with our system.

Conclusion
We present a system that combines a high-level semantic API with a DAgger-inspired interactive training flow that enables training of useful ML policies for video game testing in a wide variety of genres. We have released an open source library as a functional illustration of our system. No ML expertise is required and training of agents for test applications often takes less than an hour on a single developer machine. We hope that this work will help inspire the development of ML techniques that can be deployed in real-world game-development flows in ways that are accessible, effective, and fun to use.

Acknowledgements
We’d like to thank the core members of the project: Dexter Allen, Leopold Haller, Nathan Martz, Hernan Moraldo, Stewart Miles and Hina Sakazaki. Training algorithms are provided by TF Agents, and on-device inference by TF Lite. Special thanks to our research advisors, Olivier Bachem, Erik Frey, and Toby Pohlen, and to Eugene Brevdo, Jared Duke, Oscar Ramirez and Neal Wu who provided helpful guidance and support.

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Posted by Mauricio Delbracio, Research Scientist and Sungjoon Choi, Software Engineer, Google Research

Despite recent leaps in imaging technology, especially on mobile devices, image noise and limited sharpness remain two of the most important levers for improving the visual quality of a photograph. These are particularly relevant when taking pictures in poor light conditions, where cameras may compensate by increasing the ISO or slowing the shutter speed, thereby exacerbating the presence of noise and, at times, increasing image blur. Noise can be associated with the particle nature of light (shot noise) or be introduced by electronic components during the readout process (read noise). The captured noisy signal is then processed by the camera image processor (ISP) and later may be further enhanced, amplified, or distorted by a photographic editing process. Image blur can be caused by a wide variety of phenomena, from inadvertent camera shake during capture, an incorrect setting of the camera’s focus (automatic or not), or due to the finite lens aperture, sensor resolution or the camera’s image processing.

It is far easier to minimize the effects of noise and blur within a camera pipeline, where details of the sensor, optical hardware and software blocks are understood. However, when presented with an image produced from an arbitrary (possibly unknown) camera, improving noise and sharpness becomes much more challenging due to the lack of detailed knowledge and access to the internal parameters of the camera. In most situations, these two problems are intrinsically related: noise reduction tends to eliminate fine structures along with unwanted details, while blur reduction seeks to boost structures and fine details. This interconnectedness increases the difficulty of developing image enhancement techniques that are computationally efficient to run on mobile devices.

Today, we present a new approach for camera-agnostic estimation and elimination of noise and blur that can improve the quality of most images. We developed a pull-push denoising algorithm that is paired with a deblurring method, called polyblur. Both of these components are designed to maximize computational efficiency, so users can successfully enhance the quality of a multi-megapixel image in milliseconds on a mobile device. These noise and blur reduction strategies are critical components of the recent Google Photos editor updates, which includes “Denoise” and “Sharpen” tools that enable users to enhance images that may have been captured under less than ideal conditions, or with older devices that may have had more noisy sensors or less sharp optics.

A demonstration of the “Denoise” and “Sharpen” tools now available in the Google Photos editor.

How Noisy is An Image?
In order to accurately process a photographic image and successfully reduce the unwanted effects of noise and blur, it is vitally important to first characterize the types and levels of noise and blur found in the image. So, a camera-agnostic approach for noise reduction begins by formulating a method to gauge the strength of noise at the pixel level from any given image, regardless of the device that created it. The noise level is modeled as a function of the brightness of the underlying pixel. That is, for each possible brightness level, the model estimates a corresponding noise level in a manner agnostic to either the actual source of the noise or the processing pipeline.

To estimate this brightness-based noise level, we sample a number of small patches across the image and measure the noise level within each patch, after roughly removing any underlying structure in the image. This process is repeated at multiple scales, making it robust to artifacts that may arise from compression, image resizing, or other non-linear camera processing operations.

The two segments on the left illustrate signal-dependent noise present in the input image (center). The noise is more prominent in the bottom, darker crop and is unrelated to the underlying structure, but rather to the light level. Such image segments are sampled and processed to generate the spatially-varying noise map (right) where red indicates more noise is present.

Reducing Noise Selectively with a Pull-Push Method
We take advantage of self-similarity of patches across the image to denoise with high fidelity. The general principle behind such so-called “non-local” denoising is that noisy pixels can be denoised by averaging pixels with similar local structure. However, these approaches typically incur high computational costs because they require a brute force search for pixels with similar local structure, making them impractical for on-device use. In our “pull-push” approach¹, the algorithmic complexity is decoupled from the size of filter footprints thanks to effective information propagation across spatial scales.

The first step in pull-push is to build an image pyramid (i.e., multiscale representation) in which each successive level is generated recursively by a “pull” filter (analogous to downsampling). This filter uses a per-pixel weighting scheme to selectively combine existing noisy pixels together based on their patch similarities and estimated noise, thus reducing the noise at each successive, “coarser” level. Pixels at coarser levels (i.e., with lower resolution) pull and aggregate only compatible pixels from higher resolution, “finer” levels. In addition to this, each merged pixel in the coarser layers also includes an estimated reliability measure computed from the similarity weights used to generate it. Thus, merged pixels provide a simple per-pixel, per-level characterization of the image and its local statistics. By efficiently propagating this information through each level (i.e., each spatial scale), we are able to track a model of the neighborhood statistics for increasingly larger regions in a multiscale manner.

After the pull stage is evaluated to the coarsest level, the “push” stage fuses the results, starting from the coarsest level and generating finer levels iteratively. At a given scale, the push stage generates “filtered” pixels following a process similar to that of the pull stage, but going from coarse to finer levels. The pixels at each level are fused with those of coarser levels by doing a weighted average of same-level pixels along with coarser-level filtered pixels using the respective reliability weights. This enables us to reduce pixel noise while preserving local structure, because only average reliable information is included. This selective filtering and reliability (i.e. information) multiscale propagation is what makes push-pull different from existing frameworks.

This series of images shows how filtering progresses through the pull-push process. Coarser level pixels pull and aggregate only compatible pixels from finer levels, as opposed to the traditional multiscale approaches using a fixed (non-data dependent) kernel. Notice how the noise is reduced throughout the stages.

The pull-push approach has a low computational cost, because the algorithm to selectively filter similar pixels over a very large neighborhood has a complexity that is only linear with the number of image pixels. In practice, the quality of this denoising approach is comparable to traditional non-local methods with much larger kernel footprints, but operates at a fraction of the computational cost.

Image enhanced using the pull-push denoising method.

How Blurry Is an Image?
An image with poor sharpness can be thought of as being a more pristine latent image that was operated on by a blur kernel. So, if one can identify the blur kernel, it can be used to reduce the effect. This is referred to as “deblurring”, i.e., the removal or reduction of an undesired blur effect induced by a particular kernel on a particular image. In contrast, “sharpening” refers to applying a sharpening filter, built from scratch and without reference to any particular image or blur kernel. Typical sharpening filters are also, in general, local operations that do not take account of any other information from other parts of the image, whereas deblurring algorithms estimate the blur from the whole image. Unlike arbitrary sharpening, which can result in worse image quality when applied to an image that is already sharp, deblurring a sharp image with a blur kernel accurately estimated from the image itself will have very little effect.

We specifically target relatively mild blur, as this scenario is more technically tractable, more computationally efficient, and produces consistent results. We model the blur kernel as an anisotropic (elliptical) Gaussian kernel, specified by three parameters that control the strength, direction and aspect ratio of the blur.

Gaussian blur model and example blur kernels. Each row of the plot on the right represents possible combinations of σ0, ρ and θ. We show three different σ0 values with three different ρ values for each.

Computing and removing blur without noticeable delay for the user requires an algorithm that is much more computationally efficient than existing approaches, which typically cannot be executed on a mobile device. We rely on an intriguing empirical observation: the maximal value of the image gradient across all directions at any point in a sharp image follows a particular distribution. Finding the maximum gradient value is efficient, and can yield a reliable estimate of the strength of the blur in the given direction. With this information in hand, we can directly recover the parameters that characterize the blur.

Polyblur: Removing Blur by Re-blurring
To recover the sharp image given the estimated blur, we would (in theory) need to solve a numerically unstable inverse problem (i.e., deblurring). The inversion problem grows exponentially more unstable with the strength of the blur. As such, we target the case of mild blur removal. That is, we assume that the image at hand is not so blurry as to be beyond practical repair. This enables a more practical approach — by carefully combining different re-applications of an operator we can approximate its inverse.

Mild blur, as shown in these examples, can be effectively removed by combining multiple applications of the estimated blur.

This means, rather counterintuitively, that we can deblur an image by re-blurring it several times with the estimated blur kernel. Each application of the (estimated) blur corresponds to a first order polynomial, and the repeated applications (adding or subtracting) correspond to higher order terms in a polynomial. A key aspect of this approach, which we call polyblur, is that it is very fast, because it only requires a few applications of the blur itself. This allows it to operate on megapixel images in a fraction of a second on a typical mobile device. The degree of the polynomial and its coefficients are set to invert the blur without boosting noise and other unwanted artifacts.

The deblurred image is generated by adding and subtracting multiple re-applications of the estimated blur (polyblur).

Integration with Google Photos
The innovations described here have been integrated and made available to users in the Google Photos image editor in two new adjustment sliders called “Denoise” and “Sharpen”. These features allow users to improve the quality of everyday images, from any capture device. The features often complement each other, allowing both denoising to reduce unwanted artifacts, and sharpening to bring clarity to the image subjects. Try using this pair of tools in tandem in your images for best results. To learn more about the details of the work described here, check out our papers on polyblur and pull-push denoising. To see some examples of the effect of our denoising and sharpening up close, have a look at the images in this album.

Acknowledgements
The authors gratefully acknowledge the contributions of Ignacio Garcia-Dorado, Ryan Campbell, Damien Kelly, Peyman Milanfar, and John Isidoro. We are also thankful for support and feedback from Navin Sarma, Zachary Senzer, Brandon Ruffin, and Michael Milne.

¹ The original pull-push algorithm was developed as an efficient scattered data interpolation method to estimate and fill in the missing pixels in an image where only a subset of the pixels are specified. Here, we extend its methodology and present a data-dependent multiscale algorithm for denoising images efficiently. ^↩

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