Television – Bandwidth reduction system – Data rate reduction
Patent
1996-10-31
1998-11-24
Britton, Howard W.
Television
Bandwidth reduction system
Data rate reduction
348398, 348424, 348421, H04N 726, H04N 750
Patent
active
058414738
DESCRIPTION:
BRIEF SUMMARY
This invention is in the field of data compression, and is specifically directed to the compression of temporal or spatial sequences of images.
BACKGROUND OF THE INVENTION
The combination of modern data compression and decompression technology with the ever-increasing capability of desktop workstations and personal computers has enabled important changes in the transmission, storage, and use of information. These techniques have been applied to three-dimensional images, where the third dimension may correspond to time (e.g., a motion picture) or to space (e.g., a cross-sectional view of a complex three-dimensional object or volume). For example, the telecommunication and viewing of audio-visual images and sequences, such as over the Internet, is now commonplace. In addition, data compression techniques are widely used in the archiving and retrieval of data bases, including large three-dimensional graphics data bases such as useful in the seismic prospecting field.
One type of well-known data compression approach is referred to as lossless data compression, in which repetitive bits in the digital representation of information are combined. An example of lossless compression is commonly referred to as "byte packing". The defining characteristic of lossless compression is that an exact copy of the input information is obtained upon decompression. Because of this feature, lossless compression is useful in the storage and communication of computer programs, numerical databases, and other information in which exact replication is a requirement. However, the compression ratio achieved from modern lossless compression is relatively low. As a result, the application of only lossless compression to massive data representations, such as motion pictures and seismic surveys, is inadequate to allow real-time communication of the sequences, or significant storage savings.
Lossy data compression techniques are also known in the art. The defining characteristic of lossy data compression is, of course, that the decompressed information cannot exactly match that of the original information; in other words, some information is lost in the compression of the input information. Lossy compression techniques can provide very high compression ratios, however, and as such are often used when the information to be compressed does not require exact bit-for-bit replication upon decompression. As a result, lossy data compression techniques are useful in the compression of video or graphics images, audio signals, and other digital representation of analog information. Lossy compression techniques are often used in combination with lossless compression in these applications.
Typically, lossy data compression techniques may be considered as low-pass filters of the input information. High frequency effects in graphics images generally correspond to the edges of shapes in the image, and as such the application of lossy compression to these images is generally reflected as a loss of resolution at the edges of image elements, or by a loss of resolution in those portions of the image that change over time or space, with the low-frequency portions remaining substantially accurate when decompressed. Typically, the compression technique may therefore be considered as performed in three stages: encoding of the information into a compressible form (generally involving quantization of the information), decomposition of the encoded images, and thresholding of the decomposed images to eliminate the storage or communication of low coefficient values.
In order to obtain accurate decomposition in compression, many relatively complex transformation processes are known in the art. One type of decomposition, commonly referred to as JPEG (joint Photographic Experts Group) compression, divides each image into blocks, and applies a Discrete Cosine Transform to each block to produce arrays of coefficients which are then quantized and subjected to a difference algorithn. Another approach, referred to as fractal compression, divides each image into pixel group
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Chui Charles K.
Wang Jianrong
Britton Howard W.
Software for Image Compression, N.V.
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