Image analysis – Image compression or coding – Pyramid – hierarchy – or tree structure
Reexamination Certificate
2000-03-29
2003-11-04
Couso, Jose L. (Department: 2621)
Image analysis
Image compression or coding
Pyramid, hierarchy, or tree structure
Reexamination Certificate
active
06643406
ABSTRACT:
TECHNICAL FIELD OF INVENTION
The present invention relates generally to the field of signal processing and, more particularly, to a method and apparatus for linear filtering with filters of any desired shape and length using the wavelet transform as the computation engine by modifying the basis functions. Specifically, the method and apparatus modify the basis function of the wavelet and/or inverse wavelet transform(s) by convolving it with the desired filter, thereby forming a modified wavelet and/or inverse wavelet transform(s).
BACKGROUND OF THE INVENTION
The present invention method and apparatus are applicable to a variety of signal processing related fields, including, but not limited to the following: radar processing; geophysics processing (e.g., meteorology, oceanography, geodesy, and seismology); audio processing; telephonic processing (e.g., land-line, remote, cellular, digital, and analog); personal digital assistants (PDA) processing; wireless application protocol (WAP) processing; teleconferencing processing; data storage, retrieval and transmission processing; networks and communication processing; facsimile processing; video processing; multi-media processing; still-image processing; medical image processing (e.g., ultrasound, radiology and mammography); computer processing; internet and intranet processing; and/or other sensory data processing. The present invention relates to signal processing and more particularly to the linear filtering of data signals, such as image signals which may be used in image processing methods and apparatuses.
The present invention can be used for linear filtering digital signals and images with desired filters of any shape and length using the forward wavelet/inverse wavelet transform hardware and software as the only computation engine. Except for an initial (one time) modification of the wavelet basis functions all of the computation to perform linear filtering in the present invention can be done either in the forward wavelet transform step, in the inverse wavelet transform step or in both the forward and inverse wavelet transforms. While the present invention is applicable to a myriad of technically related fields, including the ones mentioned above, for purposes solely for illustration, the present invention may be incorporated with data compression and decompression methods and apparatuses, which generally relate to the compression, decompression, transmission, and storage of audio, still-image, video, and multi-media data in digital form in the applicable one, two, and three dimensional signal formats.
1. DFT AND DCT
By way of background, the discrete Fourier transform (DFT) and the discrete cosine transform (DCT) (as discussed in U.S. Pat. No. 5,453,945 to Tucker et al.; U.S. Pat. No. 5,757,974 to Impagliazzo; U.S. Pat. No. 5,706,220 to Vafai et al., and U.S. Pat. No. 5,838,377 to Greene, and herein incorporated by reference) are mathematical algorithms that break down signal data or image into their sine/cosine wave components. The sine/cosine wave components are also known as the transform coefficients. The magnitude of the transform coefficients determines the appropriate amount of the corresponding basis function so when pieced back together, these components reproduce the original signal or image. Heretofore known embodiments of signal or image compression apparatuses often use the DFT or DCT to break signals or images into their sinusoidal components and save only the largest components to compress the data. The rest of the components are discarded or represented with fewer bits. This can result in a significant loss of information since the signal or image's information is spread over all of its sinusoidal components. For example, the sharp details of an X-ray image are smoothed when the image is compressed at high levels, thus reducing the value of the image. If a lower level of compression is used (i.e. less-lossy) data storage requirements are increased and a greater amount of time is required for data transmission. Similar problems occur in other data compression applications.
Conventional image compression techniques that are associated with the Joint Photograph Expert Group (JPEG) or Moving Picture Expert Group (MPEG) standards are widely used in many of the current imaging devices but are unable to meet the increasing demand for higher compression ratios and greater image fidelity needed by many applications. For example, utilizing new communication channels with lower bandwidth. The JPEG technique, based on the DCT, divides the image into a series of 8×8 pixel blocks and the DCT is computed for each block producing an average value and 63 frequency values. These values can be quantized to produce a minimum set representing each 8×8 block. Because the image is divided into 8×8 blocks, the approach tends to mask details, interrupt continuous lines, and cause discontinuities at the borders thereby lowering image fidelity in the reconstructed image.
Thus, DCT based image compression has two serious disadvantages, namely, a blocking effect and a mosquito noise effect (also referred to as corona effect). The blocking effect is attributable principally to a quantization error in the generation of lower frequency coefficients while mosquito noise is attributable to a quantization error in the generation of higher frequency coefficients. As can be appreciated by one skilled in the art, wavelet transform coding was recently introduced and developed to overcome these disadvantages.
2. COMPRESSION
Many different data compression techniques exist in the prior art. Compression techniques can be divided into two broad categories, lossy coding and lossless coding. Lossy coding involves coding that results in the loss of information, such that there is no guarantee of perfect reconstruction of the original data. The goal of lossy compression is that changes to the original data are done in such a way that they are not objectionable or detectable. In lossless compression, all the information is retained and the data is compressed in a manner which allows for perfect reconstruction.
For purpose of illustration, consider an image comprising a gray-scale representation of a photograph displayed by a rectangular array of picture elements (“pixels” or “pels”) arranged in 1000 rows by 1000 columns (1,000,000 pixels). Each pixel typically consists of or is represented by 8 bits which are used to encode 256 possible intensity levels at the corresponding point on the photograph. Hence, without compression, transmission of the photograph requires a total of 8×10
6
bits (1 million bytes or 1 Megabyte) be sent over the communication link. A typical telephone line is capable of transmitting about 56,000 bits per second (bps); hence the picture transmission would require nearly 2½ minutes. Transmission times of this magnitude are unacceptable.
The bit budget may be a function of a variety of factors, such as the product of the desired bit rate (e.g., 0.25 bits/pel) and the number of coefficients (e.g., 512 by 512 pixel image), the capacity of a modem by which the coded bit stream will be transmitted, or the capacity of a disk or tape used to store the coded bit stream. For instance, a single, relatively modest-sized image, having 640 by 480 pixels and a full-color resolution of 24 bits per pixel (three 8-bit bytes per pixel), occupies nearly a megabyte of data. At a resolution of 1024 by 768 pixels, a 24-bit color screen requires 2.3 megabytes of memory to represent. A 24-bit color picture of an 8.5 inch by 11 inch page, at 300 dots per inch, requires as much as 25 megabytes to represent.
As mentioned above, an image may be compressed during transmission to reduce the bit rate of an input image or to increase the efficiency of a storage device for storing image data. In most cases, however, picture quality deteriorates since the integrity of the image data is degraded by the image compression. The ultimate object of image compression is to maintain picture quality by eliminating any appreciable viewin
Cottrell F. Richard
Hajjahmad Ibrahim
Wober Munib
Couso Jose L.
Polaroid Corporation
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