H.264 vs. MJPEG in Embedded Vision – all You Need to Know

Embedded vision technology is revolutionizing numerous industries, enabling smart surveillance, robotics, autonomous vehicles, and more. As the demand for high-quality video processing in embedded systems grows, efficient video compression techniques become imperative.

Two popular video compression standards, MJPEG and H.264, are commonly used in embedded vision applications.

In this article, we will delve into the key aspects of video compression, explore the differences and benefits of MJPEG and H.264, and provide guidance for choosing the appropriate compression method for your application.

What is Video Compression?

Video compression is essential for embedded vision applications, allowing efficient video stream storage, transmission, and processing. Eliminating redundant information minimizes bandwidth requirements and lowers storage costs, making high-resolution video capture and analysis feasible on resource-constrained embedded devices. It is the art and science of reducing the size of a video file without significantly compromising its quality, enabling applications like drones, industrial automation, and telemedicine to be realized within the confines of limited processing power, bandwidth, and storage.

Why is Video Compression Required in Embedded Vision?

Video compression enables efficient utilization of limited resources. It ensures smooth video streaming and real-time analysis by reducing the data size with minimal compromise to the quality of the visual information.

Video compression has become an indispensable feature for several reasons:

Effective storage: Video files that have not been compressed can be huge. A few minutes of uncompressed 1080p video might easily occupy over a gigabyte of storage. For example, surveillance cameras and industrial inspection equipment sometimes require storing hours or days’ worth of footage. Storage needs would become impractical and expensive without effective compression.

Network bandwidth: Many embedded vision systems are networked devices that transfer visual data for further processing or storage. High-definition video can quickly tax network resources, resulting in delays or even system failure. In addition, camera interfaces like USB have an upper limit regarding bandwidth. Compression minimizes the amount of data that must be delivered, allowing for better utilization of available bandwidth.
Energy efficiency: Video data processing and transmission are both energy-intensive operations. Power conservation is critical for battery-powered equipment such as portable medical imaging devices or drones. Video compression methods lower the computational and data transmission load, saving power.
Cost-efficiency: Video compression enables a more cost-effective system by reducing storage, bandwidth, and processing needs. Businesses can build more complex features or devote attention to other key system areas without suffering large expenses.
Retention of quality: Modern compression methods, like H.264, are meant to keep the original video quality as much as possible while significantly reducing data. This is especially crucial in applications such as medical imaging, where a loss of detail might have significant consequences.

What is MJPEG?

MJPEG (Motion JPEG) is a video compression format that encodes each video frame as a separate JPEG image. It provides high-quality images and uses no inter-frame prediction. It is ideal for scenarios where picture quality is important or when individual frames must be extracted for further analysis. Examples include security and surveillance applications, where MJPEG allows for precise image capture of individual frames for evidentiary purposes. Additionally, MJPEG is computationally simple, making it suitable for low-power embedded systems that require real-time encoding and decoding.

Overview: MJPEG (Motion JPEG) uses JPEG to compress individual video frames, making each frame an independent entity.
Benefits:
– Simplicity: The simplicity of MJPEG makes it easier to modify individual frames, which is useful in applications such as video editing.
– Low latency: The low latency of MJPEG benefits real-time systems like robotics and drone navigation.
– Quality retention is critical in industries such as medical imaging, where quality cannot be compromised.
– Low CPU load: Excellent for devices with low processing power.
Industry examples:
– Surveillance: MJPEG is frequently used in IP cameras.
– Industrial automation: MJPEG is used for real-time analysis by quality inspection systems.
– Medical imaging: MJPEG is utilized in real-time imaging applications for endoscopy and other medical procedures.
Drawbacks: Compression rate: The rate of compression of MPJEG is not as high as other compression algorithms.

What is H.264?

H.264, also known as MPEG-4 Part 10 or AVC (Advanced Video Coding), is a widely adopted video compression standard renowned for its ability to deliver high-quality video with exceptional compression efficiency. It achieves this by exploiting both spatial and temporal redundancies in videos and between frames, resulting in minimized data storage and transmission requirements. With extensive codec support and broad compatibility, H.264 is used in various applications such as video conferencing, surveillance cameras, and streaming platforms.

Overview: H.264 provides a balance between compression efficiency and quality, using advanced algorithms to store differences between frames.
Benefits:
– High compression efficiency: Perfect for situations when bandwidth and storage are in short supply.
– Flexibility: H.264 is flexible to a variety of use cases because of its wide range of profiles.
– Advanced features: In networked systems, features like error resilience are helpful.
– High-quality video: Retains quality even at low bit rates.
Industry examples:
– 264 is often used in consumer electronics like smartphones and tablets.
– Streaming Services: H.264 is employed by platforms such as Netflix and YouTube. In embedded vision, it can be used in automated sports broadcasting applications.
– Due to its efficiency, H.264 is frequently used in in-car entertainment systems and dashcams.
Drawbacks:
– Computational Load: Because H.264 utilizes advanced techniques to compare sequential frames and encode only the differences between them, H.264 requires a much higher computational load than MJPEG.
– Latency: H.264 achieves higher compression efficiency through temporal compression, which may incur a higher latency.

MJPEG vs. H.264

The choice between MJPEG and H.264 codecs for embedded vision applications largely depends on specific needs. MJPEG is better suited for scenarios prioritizing image quality, such as video surveillance, but it consumes more storage space and bandwidth than H.264. By comparison, H.264 is highly efficient in minimizing bandwidth requirements without compromising visual quality, perfect for applications involving real-time video transmission, such as robotics or autonomous vehicles.

Comparison Between MJPEG and H.264

Feature	MJPEG	H.264
Quality	Higher	Lower
Compression	Less efficient	More efficient
CPU Usage	Lower for decoding	Higher for decoding
Real-time Capability	Better	Good but not optimal
Flexibility	Less flexible	Highly flexible
Latency	Lower	Higher

Quality and compression: MJPEG provides improved quality at the expense of inefficient compression. H.264, on the other hand, offers much more efficient compression but at a cost of higher latency and computational load.
CPU utilization: MJPEG uses less CPU power for decoding, which may be useful in low-power systems. H.264 needs additional computing power, particularly while encoding.
Real-time uses: MJPEG has a low latency requirement, so it is suited for real-time systems. H.264 is better suited for situations where storage and bandwidth are more important.
Flexibility: H.264 provides various profiles and features, making it applicable to various circumstances. Since it is less versatile, MJPEG is easier to build but has less functionality.

How to Choose the Right Compression Method for Your Application?

Selecting the appropriate compression method for an embedded vision application requires careful consideration of multiple factors. Conducting comprehensive tests and evaluations on sample data can help determine which compression method best suits your application’s requirements.

Examine the requirements: First and foremost, determine what you require. Is low latency essential? Do you require high-resolution images? Or maybe you’re limited by bandwidth and storage? Knowing your needs will help you choose which compression standard is ideal for you.
Hardware requirements: Hardware capabilities can greatly influence the chosen compression. For example, if your machine has limited CPU power, the lower processing needs of MJPEG may be more appropriate. You also need to consider the bandwidth capabilities of your camera interface. The desired compression algorithm may greatly influence the SOC selection for your embedded product. For example, if H.264 compression is selected, but the desired SOC does not support H.264 compression in hardware, this can lead to a very high (and highly inefficient) CPU computational burden.
Assess the software ecosystem: Take note of the software tools and libraries you will employ. Some platforms support one compression standard better, which may ease development and maintenance.
Network configuration: The quality and dependability of your network may also influence your choice. If you work in a low bandwidth or high packet loss environment, H.264’s efficiency and error recovery features may be more useful.
Adaptability: Consider the future. Will your compression strategy scale with your system as it grows? H.264 often provides more scalability capabilities, such as adaptive bitrate streaming.
Energy consumption: Power efficiency is important in battery-powered devices such as drones and portable medical equipment. Consider how each compression method affects battery life.
Conditions for latency: Low latency is typically crucial for real-time applications such as video conferencing or robots. MJPEG often has reduced latency, making it an excellent choice for such circumstances.
Cost impacts: Your decision might influence both immediate and long-term expenditures. While MJPEG may be easier and less expensive to adopt initially, H.264 may offer long-term cost reductions due to lower bandwidth and storage requirements.
Test and repeat: Last, but not least, field testing is priceless. Make prototypes of both compression methods and test their performance in real-world settings. This will bring insights that theoretical analysis alone cannot offer.

Conclusion

In embedded vision applications, video compression plays a critical role in optimizing resource utilization, achieving efficient storage and transmission, and enabling real-time video processing. Depending on the application scenario, compression standards such as MJPEG and H.264 can offer unique benefits. Understanding the differences between these two standards helps businesses make informed decisions when implementing video compression in their systems.

By carefully considering the specific needs of their application, businesses can select an appropriate compression method to maximize resource efficiency while delivering high-quality video output. With the right compression technique, embedded vision applications can unlock the true potential of smart surveillance, robotics, autonomous vehicles, and various other emerging technologies.

TechNexion offers tailor-made solutions that enable businesses to optimize the video processing needs in their embedded vision systems. We offer solutions that support both H.264 and MJPEG for unparalleled compression efficiency. Plus, we design our solutions to scale with your growing business needs – enabling faster go-to-market and deployment of your products. Learn more about our embedded vision solutions here.