How to Avert a Compression Depression

While bandwidth is widening, larger video systems and more advanced megapixel cameras are continuing to push the throughput limits of network piping. Fortunately, new compression methods such as H.264 are available to help keep surveillance data flowing.

When Less May Be More
Analyzing the video signal separates the signal into many parts, or subparts, and is classified by the importance in reference to the image’s visual quality.

Following this signal analysis, the next part is the quantizer. Quantization is simply the process of decreasing the number of bits needed to store a set of values, or transformed coefficients as they are called in data compression language.

Since quantization is a many-to-one mapping and reduces the precision of those values, it is known as a lossy process (as opposed to lossless) and is the main source of compression in most image coding schemes. There is a trade-off between image quality and degree of quantization.[IMAGE]dumies1-1.jpg[/IMAGE]

A large quantization step size can produce unacceptably large image distortion.

Lossy compression actually eliminates some of the data in the image and, therefore, provides greater compression ratios than lossless compression.

Lossless, on the other hand, consists of those techniques guaranteed

to generate an exact duplicate of the input data stream after a compress/ expand cycle. No information is lost, hence the name lossless. However, this method can only achieve a modest amount of compression. The lossless compression of images is important in fields such as medical imaging and remote-sensing where data integrity is essential.

Typically, compression ratios for lossless codes, including variable length encoding, are listed as an average of 4:1 compression. In variable length encoding, prior to the writing of the image, the information is aligned according to frequency, which plays an important role in the image compression process. For the most part, lower frequencies, which occur more often, are placed to the front while higher frequencies are placed at the end.

In any file, certain characters are used more than others. In general, we can attain significant savings if we use variable- length prefix codes that take advantage of the relative frequencies of the symbols in the messages to be encoded.

The advantage of lossy methods over lossless methods is that, in some cases, a lossy method can produce a much smaller compressed file than any known lossless method, while still meeting the requirements of the application.

2 Paths: Full Image or Conditional
There are several methods of analyzing a video image. The first is full image compression. This approach usually relates to Joint Photographic Experts Group (JPEG) and wavelet compression schemes in which the entire image is analyzed, compressed and transmitted.[IMAGE]dumies3-1.jpg[/IMAGE]

In most cases, this form of analyzing an image can only provide a limited amount of compression, meaning larger image file sizes and increased bandwidth issues.

For the most part full image compression incorporates irrelevancy reduction methods. Irrelevancy reduction omits parts of the video signal that are not noticed or perceived by the signal receiver, which in this case is the human eye.

Through the research of Human Visual Systems (HVS), it has been proven that small color changes are perceived less accurately than small changes in brightness, so why brother saving this information? It is also known that low frequency changes are more noticeable to the human eye than high frequency changes. (Low frequencies control the coarser or more noticeable conditions of a video image whereas higher frequencies are usually related to the finer details of a video image.)

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