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Unique in the Market: The New Beyond Features from Basler

The ace 2 Pro product line offers unique ace 2 features that can provide you with immediate added value in terms of increased performance and cost savings in the operation of your vision system. The Beyond features are especially characterized by the fact that their functionality is unique in the market and often even patented or patent-pending.

Faster Frame Rates on GigE with Compression Beyond

Does the bandwidth of Gigabit Ethernet limit the performance of your system?

Benefit from more efficient bandwidth usage with Basler's new in-camera feature Compression Beyond.

Maximum GigE with minimum effort: Compression Beyond

Gigabit Ethernet for Machine Vision (GigE Vision) offers many advantages in the field of professional image processing. For example, multi-camera systems can be optimally implemented and distances of up to 100 meters can be bridged with cost-effective network cables. The data rate of up to 120 MB/s is perfectly sufficient for many image processing applications.

Nevertheless, the current trend towards higher resolutions and faster frame rates is increasingly leading to the desire for higher data rates. However, the leap to ten times the data rate made possible by new interface technologies such as CoaXPress 2.0 and 10GigE is often not necessary for these applications and would be associated with significantly higher costs.

The solution: a more efficient use of bandwidth. By compressing data directly in the camera frame rates in the range of two to three times become reality - depending on the respective image content.

ace 2 models with Compression Beyond

How does Compression Beyond work?

The image data is compressed directly in the camera using a powerful FPGA.

The basic principle of lossless compression of image data is based on the use of redundancies. Similar to the previously widespread Morse codes, the compression of image data also involves so-called coding. This means that codes are assigned to individual messages or data blocks. More frequently occurring bit patterns receive shorter codes, less frequently occurring bit patterns receive longer codes. On average, the amount of data can be optimized by this redundancy reduction.

As a result, images that contain many redundancies are more compressible. The actual performance of the compression depends on the respective image content.

Image Comparison

Original image without Compression Beyond
Original image without Compression Beyond
Image with Compression Beyond at compression factor 2.6
Image with Compression Beyond at compression factor 2.6

Encoding via Basler Codebook

These encodings of the data information are carried out using codebooks. The Compression Beyond feature is based on a codebook developed by Basler and optimized for Machine Vision applications and is therefore unique in the market. The principle of entropy coding enables lossless compression of image data. The image quality is therefore maintained in full, even though the amount of data is considerably reduced at the same time.

In addition, the image data can also be saved in compressed format. This saves storage capacity and consequently costs.

Not every application requires the same amount of compression. To give our customers as much leeway as possible, we go one step further: In order to find the optimal balance between image size and image quality for your application, you can individually adjust the compression factor and also choose an even stronger, but then lossy compression to achieve the best result for your requirements.

Pixel Beyond – Pixel Size on Demand

You want to reduce the resolution but keep the field of view? Common binning factors don't allow this? You want a higher frame rate or would like to replace a discontinued sensor as simply as possible?

Adapt your sensor to your requirements - with Pixel Beyond.

Pixel sizes as desired – Pixel Beyond makes it possible

If you summarize the values of neighboring pixels of a sensor, you speak of binning. There can be different reasons for binning: some users want to increase the brightness in their images, others want to reduce the amount of data. Some need higher frame rates, others are looking for an uncomplicated replacement for their discontinued sensor.

Binning can help with all these objectives. With binning, you can reduce the resolution while maintaining the field of view and improve certain sensor values such as the signal-to-noise ratio (SNR) and dynamic range. In theory, the amount of data to be processed becomes smaller, the frame rate often higher, and the exposure time shorter or the images brighter. In practice, however, the major drawback is that conventional binning is based solely on integer factors.

ace 2 models with Pixel Beyond Feature

What are the advantages of Pixel Beyond?

Conventional binning and the limitations of integer factors

Conventional binning takes place at the sensor level. Since this binning method only allows integer multiples such as 2x2 or 3x3, the resolution can only be reduced in big leaps - for example to ¼ or 1/9. However, this sudden reduction of the resolution is often not useful or even obstructive. The optimal target is often in between, but is not realizable with integer binning.

Pixel Beyond by Basler offers a decisive advantage: it allows the use of decimal numbers in addition to integer factors. The result: significantly more flexibility! Thanks to Pixel Beyond, all conceivable resolutions between ¼ and the respective maximum sensor resolution can be realized as desired. Using a powerful FPGA, this pre-processing of the pixels takes place directly in the camera. A novel interpolation method developed by Basler serves as the basis.

Pixel Beyond and the more flexible scalability of pixel sizes

The benefit for you as a customer: By individually adjusting the resolution, you can use the available bandwidth more efficiently and significantly reduce the amount of data that has to be processed on the host side.

Thanks to Pixel Beyond, you can realistically simulate different sensors. For example, in the case of a discontinued sensor, this enables uncomplicated replacement without the need for time-consuming redesign of your vision system. Other approaches that attempt to reproduce sensor characteristics using conventional interpolation methods such as nearest-neighbor or (bi-)linear, on the other hand, often lead to false EMVA data.

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