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Is the Future of Image Processing in Software?


With hardware image processing, video quality will always be limited. Will the software ISP open a world of image processing possibilities?

Traditionally, Image Signal Processors (ISPs) are typically implemented as hardware components. But camera manufacturers are starting to recognize that improving software is the best strategy for improving image quality. 

Google Pixel, which boasts some of the world’s leading image quality, has taken this approach. From the Pixel 2 to the Pixel 6, Google has kept the same camera and lens hardware, but focused their improvements instead on their image processing software. It seems that there’s a glass ceiling to how much we can improve video quality with affordable hardware, but we can still make huge upgrades to video quality with software alone. 

As the world moves towards software, can we expect software ISPs to become the new normal? First, let’s understand why ISPs are hardware components. There are a few reasons:

Performance: Image processing tasks, such as noise reduction, demosaicing, color correction, and image enhancement, require real-time processing and high computational power. Dedicated hardware components are optimized for these tasks and can deliver the necessary performance efficiently. Software-based solutions might struggle to achieve the same level of performance due to the inherent limitations of general-purpose processors.

Power efficiency: Image processing tasks can be computationally intensive and demand significant power. Hardware ISPs are designed to be power-efficient, focusing on executing specific operations quickly and consuming minimal power. This is crucial for devices with limited battery life, such as smartphones and cameras. Software-based solutions may require more power and impact the overall battery life of the device. (Therefore, if it is being used at the edge, it needs to be implemented very efficiently.)

Latency: Many image processing tasks need to be performed in real time, especially for applications like live video streaming or real-time video processing. Hardware ISPs can process image data quickly, reducing latency and providing immediate feedback. Software-based solutions might introduce additional latency due to the overhead associated with processing instructions in software.

Integration: ISPs are often integrated into the image sensor module or the system-on-a-chip (SoC) design of a device. By having dedicated hardware, ISPs can be tightly integrated with the image sensor, enabling seamless communication and optimized performance. Integrating software-based solutions might be more challenging and may not achieve the same level of integration and efficiency.

The limitations of hardware

Limited flexibility: Hardware ISPs are designed for specific image processing tasks and are often optimized for a particular set of algorithms and operations. They may lack the flexibility to accommodate rapid changes or updates to the image processing pipeline. Modifying or adding new features to a hardware ISP typically requires redesigning and reconfiguring the hardware itself, which can be time-consuming and costly.

Scalability: Hardware ISPs are typically designed and manufactured for specific devices or platforms. Integrating them into different systems or scaling them for varying computational requirements can be challenging. Upgrading or replacing hardware ISPs may involve significant engineering efforts, especially if they are tightly integrated with other components of a system-on-a-chip (SoC) design.

Development and production costs: Designing and manufacturing dedicated hardware ISPs involve significant costs, including research and development, fabrication, and testing. The complexity of the hardware design process and the need for specialized expertise can make it expensive to produce and iterate on hardware ISPs compared to software-based solutions. This cost factor can impact the accessibility and feasibility of using hardware ISPs in certain applications.

Long development cycles: Developing and optimizing hardware ISPs can be a time-consuming process. Designing custom integrated circuits, conducting simulations, and manufacturing prototypes take time. This longer development cycle can lead to delays in incorporating the latest image processing advancements and techniques into consumer devices.

Limited adaptability: Hardware ISPs are typically designed with specific hardware architectures and constraints in mind. As a result, they may not easily adapt to new imaging technologies or sensor types without significant hardware modifications. This lack of adaptability can pose challenges in rapidly evolving imaging ecosystems.

Limited configurability: While hardware ISPs may provide some configurability options, they often have fixed functionality and parameters. This limitation may restrict the ability to tailor the image processing pipeline to specific application requirements or user preferences.

The challenges of a software approach

While software-based image processing algorithms exist and can be used to enhance images, they are typically run on general-purpose processors and lack the dedicated optimizations and performance of hardware ISPs. However, huge advancements have been made in software-based image processing, leveraging powerful processors and parallel computing techniques. 

What are the advantages of a software ISP?

Software-based image processing may offer more flexibility and adaptability in certain situations than hardware. Here are a few potential advantages of software-based approaches:

Edge AI capabilities: With the growth of AI imaging, we have seen AI on the edge get more powerful, enabling software ISP to tackle tough imaging tasks, even in real time. Software ISPs are becoming empowered by computational photography, with players like Synopsys, Cadence, and Nvidia now providing very powerful AI processing capabilities. If the trend continues, hardware ISPs could eventually become obsolete.

Flexibility: Software image processing algorithms can be easily modified or updated without requiring changes to the underlying hardware. This allows for greater flexibility in adjusting image processing parameters or adding new features. However, this flexibility often comes at the cost of performance and efficiency.

Cost: Developing and implementing software-based image processing algorithms can be more cost-effective compared to designing and manufacturing dedicated hardware components. Software solutions can be deployed on existing hardware platforms, reducing the need for specialized hardware development. In addition to the financial costs, this could also mean we reduce our hardware waste. If we can improve video quality through software updates, we can extend the life of our consumer electronics devices.

Research and prototyping: Software-based image processing is often used in research and prototyping stages to test and evaluate different algorithms or techniques. It allows researchers and developers to iterate quickly and experiment with various image processing approaches before committing to a specific hardware implementation.

Software image processing may still be in its infancy, but with all its advantages and flexibility, and with the growth of edge AI and AI imaging, it looks likely that the trend towards software ISPs is here to stay. When designed with efficiency in mind, and run on the right processing engines, software ISP can bring the best of both worlds to image processing.