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.
With conditional compression, only changes from image to image or to adjacent image are analyzed and compressed.
This method is usually associated with Moving Picture Experts Group (MPEG) and H.264 compression methods.
Redundancy reduction is accomplished by removing duplication from the signal source, which is found either within a single image or between multiple images of a video stream. The first of three redundancy reduction methods is labeled spatial reduction. This is the reduction of the correlation between neighboring pixel values. As seen in the spatial reduction diagram above, the data stream can be reduced to single values for each of the four quadrants. Although this is a very simple example, it shows one of the basic ways for redundancy reduction. The next reduction method is spectral reduction.[IMAGE]dumies5-1.jpg[/IMAGE]
This is the correlation between color planes or bands within an image. As an example, let us look at the blue sky in the spectral reduction diagram above. Many areas of that sky have the same numeric value and, therefore, the amount of stored information to reproduce that same image in the decompression mode of operation.
The last area is known as temporal reduction. This is the correlation between adjacent frames in a sequence.
This information is the basis for MPEG as well as the H.263/H.264 series of compression methods. In temporal reduction, two types of image arrangements are viewed. The first one is a full representation of the viewed image. This is known as the Iframe and is encoded as a single image, with no reference to any past or future images. In some circles it is also referred as the key-frame. The process for temporal is based on the question if there
is no movement why bother saving the information? Any movement will be detected and the compression process will begin.
Compression Fools the Human Eye
There are four methods for compression, discrete cosine transform (DCT), vector quantization (VQ), fractal compression (FC) and discrete wavelet transform (DWT).
DCT is a lossy compression algorithm that samples the image at regular intervals. It analyzes the components and discards those that do not affect the image as perceived by the human eye. JPEG, MPEG and H.264 are a few compression standards that incorporate DCT.
VQ is also a lossy compression that looks at an array of important, instead of individual, values. It then generalizes what it sees, compresses redundant information and tries to retain the desired information as close to original as possible.
FC is a form of VQ; however, this type of compression locates and compresses self-similar sections of an image.
This compression then uses fractal algorithms. (Fractal is a generalization of an information-free, object-based compression scheme rather than a quantization matrix. It uses a set that is repetitive in shape, but not in size.) DWT compresses an image by frequency ranges. It filters the entire image, both high and low frequencies, and repeats this procedures several times. Wavelet compression utilizes the entire image, which differs from many DCT methods.
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