WO1998028917A1 - Improved estimator for recovering high frequency components from compressed image data - Google Patents
Improved estimator for recovering high frequency components from compressed image data Download PDFInfo
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- WO1998028917A1 WO1998028917A1 PCT/US1997/022685 US9722685W WO9828917A1 WO 1998028917 A1 WO1998028917 A1 WO 1998028917A1 US 9722685 W US9722685 W US 9722685W WO 9828917 A1 WO9828917 A1 WO 9828917A1
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
- H04N19/80—Details of filtering operations specially adapted for video compression, e.g. for pixel interpolation
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
- H04N19/10—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding
- H04N19/169—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the coding unit, i.e. the structural portion or semantic portion of the video signal being the object or the subject of the adaptive coding
- H04N19/186—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the coding unit, i.e. the structural portion or semantic portion of the video signal being the object or the subject of the adaptive coding the unit being a colour or a chrominance component
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
- H04N19/60—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using transform coding
- H04N19/63—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using transform coding using sub-band based transform, e.g. wavelets
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
- H04N19/10—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
- H04N19/10—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding
- H04N19/102—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the element, parameter or selection affected or controlled by the adaptive coding
- H04N19/115—Selection of the code volume for a coding unit prior to coding
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
- H04N19/10—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding
- H04N19/134—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the element, parameter or criterion affecting or controlling the adaptive coding
- H04N19/146—Data rate or code amount at the encoder output
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
- H04N19/60—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using transform coding
- H04N19/61—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using transform coding in combination with predictive coding
Definitions
- This invention relates to digital signal processing techniques in general and in particular to the use of digital signal processing techniques for compression and decompression of data and for the reliable recovery of high frequency components discarded during compression of the data.
- the last step of conventional image compression schemes is to apply a lossless coding techniques such as Huffman coding or arithmetic coding.
- a lossless coding technique such as Huffman coding or arithmetic coding.
- the invention comprises a wavelet transform based method and system for estimating missing high frequency components of an image based on those frequency components which are present in the image. As mentioned above, these components may be missing because they were discarded, typically by being set to zero, during compression.
- the method of the invention can also be used where the high frequency coefficients were never present in the image to begin with.
- the method of the invention can also estimate these values based on existing pixel values, thereby enhancing the quality of the enlarged image.
- Application of the method of the invention to the enlargement of an original image starts with the assumption that the original image includes the low frequency components of the wavelet transform of an enlarged image which is four times larger than the original image.
- the high frequency coefficients of the enlarged image are then estimated from the low frequency coefficients in the original image. This is followed by the application of the inverse wavelet transform.
- the result is an image which is not only four times larger but is also of enhanced quality because the resolution has been doubled.
- This process can be performed repeatedly to obtain successively larger images.
- Application of the method of the invention to the compression of an image and to the subsequent estimation of the discarded high frequency coefficients begins with applying the wavelet transform to the original image and discarding some or all of the high frequency coefficients.
- the wavelet transform is then applied to the remaining coefficients of the wavelet transform of the original image.
- this process optionally continues for three additional levels of transformation. At this level, only those high frequency coefficients most important for perception are kept. These must then be efficiently encoded, typically by a lossless arithmetic encoding algorithm.
- the compressed file is decoded to recreate the wavelet transform up to the number of levels performed in the compression step described above.
- the inverse wavelet transform produces an image which is for example l A of the original image size.
- This image is then enlarged using the method of the invention as described above in connection with image enlargement. This results in a full size reproduced image.
- the high frequency coefficients of the first two levels of the wavelet transform of each frame are discarded resulting in frames that are 1/16 of the original size.
- the amount of data to be processed per second is 480 times smaller than with state-of-the-art approaches (e.g., MPEG-1 , MPEG-2, H.231 , H.234).
- state-of-the-art approaches e.g., MPEG-1 , MPEG-2, H.231 , H.234.
- the two-level expansion of each reproduced frame at 1/16 the original size produces a full-size video sequence of high quality.
- the ability to estimate the high frequency coefficients of the wavelet transform of the one-dimensional sound signal from the low frequency coefficients also results in high levels of compression and improved signal quality.
- processing speed is critical and compression ratio is of secondary importance.
- compression ratio is of secondary importance.
- the ability to perform compression in real-time is important.
- the Haar wavelet transform is used to compress the image on a line-by-line or block-by-block basis. Because of the simplification introduced by the use of the Haar wavelet transform, convolution of the input signal by the filter coefficients reduces to the multiplication of the sum of two adjacent pixel values by the filter coefficients followed by a one pixel shift. This procedure is one that can readily be implemented in hardware with its concomitant increase in performance.
- the system according to the invention optionally passes the retained frequency components to a low-pass synthesis filter which is biorthogonal to the low-pass analysis filter used to generate the wavelet transform of the image. This results in the generation of the low- frequency sub-band of the original image.
- the retained frequency components are also passed to an estimation system which generates an estimate of the high-frequency sub-band of the image.
- the low frequency sub-band and the high frequency sub-band are then combined at a combining stage to form the original image.
- the estimation system includes an estimation filter having a transfer function derived from the wavelet transforms high and low frequency analysis filters and their corresponding biorthogonal synthesis filters. The output of this estimation filter is then filtered by the wavelet transforms high-frequency synthesis filter before being combined, at the combining stage, with the output of the low-frequency synthesis filter.
- the output of the estimation filter is used as a starting estimate which is iteratively refined at a refining stage.
- the preferred iterative method is a conjugate gradient method.
- those high frequency coefficients which were retained rather than discarded are clamped at their known values during successive iterations of the process executed by the refining stage.
- the output of the refining stage is then filtered by the wavelet transforms high-frequency synthesis filter before being combined, at the combining stage, with the output of the low- frequency synthesis filter.
- the estimation system in yet another embodiment of the invention, used in the case in which there are no known high frequency coefficients, includes an estimation filter having a transfer function derived from the wavelet transforms high and low frequency analysis filters and their corresponding biorthogonal synthesis filters. The output of this estimation filter is then combined, at the combining stage, with the output of the low- frequency synthesis filter. In this case, there is no need to filter the output of the estimation filter with the wavelet transforms high-frequency synthesis filter before passing it to the combining stage.
- FIG. 1 shows a system for evaluating the wavelet transform of an original image, compressing the wavelet transform after discarding is high frequency components, and then recovering the discarded high frequency components to generate a reconstructed image;
- FIG. 2 shows the data recovery stage of FIG. 1 ;
- FIG. 3 shows an embodiment of the data recovery stage shown in FIG. 1 in which the estimation system processes only the low frequency coefficients to obtain an estimate of the original high frequency coefficients;
- FIG. 4 shows a prior art process for reconstructing an image based on the high frequency and low frequency components of its wavelet transform
- FIG. 5 shows the data recovery stage of FIG. 1 in which the estimation system relies on known high frequency coefficients to iteratively converge on an estimate of the missing high frequency coefficient;
- FIG. 6 shows the data recovery stage of FIG. 1 in which uses a single filter is used to estimate high frequency components with low frequency components
- FIG. 7 shows an image to be processed by the system of FIG. 1
- FIG. 8 shows the image of FIG. 7 after application of the high and low frequency analysis filters to the rows of the image
- FIG. 9 shows the image of FIG. 8 after application of the high and low frequency analysis filters to the columns of the image
- FIG. 10 shows the image of FIG. 7 after three levels of the transformation described in FIGS. 8 and 9;
- FIG. 11a shows an image such as that shown in FIG. 7 with its wavelet transform coefficients separated into three sub-bands;
- FIG. 1 lb shows two alternative paths to reconstructing the left image L from quadrants a and b;
- FIG. l ie shows two alternative paths to reconstructing the complete image / using the two sub-bands L and R.
- a system 29 incorporating the invention includes a wavelet transform stage 17 having a low frequency analysis filter 10 and a high frequency analysis filter 14 for evaluating the wavelet transform of an original image 11.
- the original image 11 can be provided by a scanner, a digital camera, a photocopy machine or any other device generating a digital signal representative of an image.
- the source of the original image 11 can also be another wavelet transform stage having a pair of analysis filters identical to the high and low frequency synthesis filters 10, 14 shown in FIG. 1.
- the wavelet transform generated by the wavelet transform stage 17 includes a low frequency portion 12 generated by convolving the original image with the low frequency analysis filter 10 and a high frequency portion 18a generated by convolving the original image through the high frequency analysis filter 14.
- a thresholding stage 16 which compares the high frequency portion 18a of the original image 11 with a preselected threshold value. If the wavelet transform coefficient associated with a portion of the image falls on one side of the threshold, that coefficient is disregarded, typically by setting it to zero. If it falls on the other side, that coefficient is retained, typically by passing it through unchanged. This thresholding process results in a diminished high frequency portion 18.
- the diminished high-frequency portion 18 and the low frequency portion 12 of the original image 11 are compressed in a conventional manner at a compression stage 13.
- the compressed image can then be stored or transmitted in its compressed form.
- the compressed image must be decompressed and the disregarded high frequency portions of the original image must be estimated.
- the decompression step is performed in a conventional fashion and is not shown in FIG. 1..
- the process of estimating the disregarded high frequency portions to generate a faithful reproduction of the original image 11a takes place in the data recovery stage 19 which is described below in connection with FIGS. 2-6.
- the wavelet transform of an original image 11 is obtained by first convolving each row of the image with two orthogonal filters: a high-pass filter 14 and a low-pass filter 10 as depicted in FIG. 1. These filters, which are also referred to as high and low frequency analysis filters respectively, are obtained from the coefficients of the scaling function defining the wavelet basis used in the transformation. Thus, the effect of the wavelet transform is to determine the high frequency and low frequency spatial energy distribution of the image.
- the low frequency spatial distribution of the original image 11 is represented by the low-pass filtered image 12 and the high frequency spatial distribution is represented by the shown in a high-pass filtered image 18.
- FIG. 1 illustrates a wavelet transform stage 17 for performing one step in the performance of successive wavelet transforms.
- Each row of the image 11 is convolved with a low-pass filter 10. However, the convolution proceeds by shifting by two pixels rather than by a single pixel. This results in a low-pass filtered image 12 half as wide as the original image 11.
- Each row of the image 11 is also convolved with a high pass filter 14 biorthogonal to the low-pass filter 10.
- the convolution again proceeds by shifting two pixels at a time rather than by a single pixel. This shift results in a high-pass filtered image 18a that is likewise half as wide as the original image.
- the high-frequency wavelet transform coefficients generating the high-pass filtered image 18 are then compared with a preselected threshold at a thresholding stage 16. Those coefficients falling to one side of this threshold are set to zero. The remaining coefficients are left unchanged.
- the reduced set of high frequency wavelet transform coefficients thus formed generates a post-threshold high-pass filtered image 18 half as wide as the original image 11 and containing those high frequency components that are important for human perception.
- the low-pass filtered image shown on the left hand side of FIG. 8 is clearly recognizable as a distorted version of the image in FIG. 7.
- the high-pass filtered image, shown on the right-hand side of FIG. 8, is, however, only barely recognizable. This is because, as mentioned above, it is the low frequency components of an image that are most important for human visual perception.
- the wavelet transform stage 17 convolves the high and low frequency analysis filters 10, 14 with the columns of the original image 11.
- the resulting four images, shown in FIG. 9, represent the wavelet transform coefficients present in four frequency bands. The lowest frequencies are in the upper-left image, the highest are in the lower-right image, and intermediate frequencies are in the remaining two images. That the upper-left image 16 is the most recognizable is also no coincidence since, as stated above, it is the low- frequency components of the image that are most important for human perception.
- FIG. 9 shows an original image
- FIG. 10 shows its wavelet transform carried out to four levels of transformation.
- the many black areas in FIG. 5 represent wavelet transform coefficients that are either zero or very small. The abundance of such coefficients is useful in providing a more compact representation of the original image 11.
- the choice of filter coefficients for the analysis filters 10, 14 determines the wavelet basis used in the transform. This basis must be well localized in both spatial and frequency domains and, in order to avoid redundancy that hinders compression, it must constitute a biorthogonal set.
- the filter coefficients for the analysis filters 10, 14 are chosen to implement the Haar wavelet transform. This is accomplished by choosing the filter coefficients for the high pass filter 14 to be 0.5 and -0.5 and the filter coefficients for the low pass filter 10 to be 0.5 and 0.5.
- convolution of the original image 11 can be accomplished efficiently on a scan line-by-scan line basis by halving the sum (or the difference in the case of the high-pass filter), of two adjacent pixel values, shifting one pixel, and repeating the process.
- the foregoing convolution algorithm is amenable to economical implementation in hardware. For applications in which real-time compression is critical, for example in photocopiers, scanners, digital cameras, or printers, the loss in compression ratio is more than offset by the increased throughput resulting from a hardware implementation of the convolution step.
- the foregoing hardware implementation can occur either in the course of compression or decompression.
- a scanner it may be more convenient to implement the convolution in hardware at the scanner and to decompress the resulting image at the host in software.
- a printer it may be more convenient to perform the compression in software at the host and to implement the decompression step in hardware at the printer.
- a photocopier which can be thought of as a scanner and printer working together, one can save memory and improve performance by implementing both the compression and the decompression in hardware.
- the threshold stage 16 incorporates a preselected threshold for determining whether or not a particular high frequency component is to be kept.
- the selection of this threshold requires consideration of the frequency dependent characteristics of human perception to determine what transform coefficients to keep in order to achieve a particular compression ratio.
- Level 2 0.16
- Level 1 0.64
- the method discards all high-pass coefficients of level 4 that are below 1% of the maximum absolute value of the coefficients of level 4.
- the other thresholds are 4% of the maximum for level 3, 16% of the maximum for level 2, and 64% of the maximum for level 1.
- the compression stage 13 encodes, using the fewest number of bits, the remaining wavelet transform coefficients associated with each sub-band. Two values must be encoded: the location within the sub-band, and the value (including the sign) of each wavelet transform coefficient. A coefficient's location within the sub-band is expressed as the distance in rows to either the previous non-zero coefficient or, in the case of the first non-zero coefficient of the sub-band, the distance to the upper left corner of the sub-band. These location values must be encoded exactly.
- a coefficient's values can be encoded efficiently by dividing the interval between the maximum and minimum threshold values into quantization bins. If the number of quantization bins is large enough, given the difference between the maximum and minimum absolute values at each level, the quantization error will not be noticeable.
- a preferred value for the number of quantization bins in this embodiment of the invention is 32.
- the next step is to apply a lossless coding scheme such as arithmetic coding to obtain the final compressed binary file.
- a lossless coding scheme such as arithmetic coding
- the first step in decompression of the compressed image is to arithmetically decode the binary compressed file. Then the coefficient values and locations are calculated and the wavelet transform of the original data, in which most if not all coefficients of the higher frequency sub-bands are zero, is recreated.
- FIG. 2 shows a data recovery stage 19 according to the invention having a low frequency synthesis filter 22 which corresponds to the low frequency analysis filter 10 of the wavelet transform stage 17 and an estimation system 38.
- the low frequency wavelet transform coefficients 12 are convolved with the low frequency analysis filter 10 and the result of the convolution is passed to a combining stage 23.
- the low frequency wavelet transform coefficients 12 are also passed to an estimation system 28 which estimates the discarded wavelet transform coefficients from the low frequency wavelet transform coefficients.
- the estimation system also uses the high frequency wavelet transform coefficients 18 that exceeded the threshold at the thresholding stage 16.
- Wavelets are functions generated from a single function ⁇ by dilations and translations.
- the basic idea of the wavelet transform is to represent an arbitrary function / as a superposition of wavelets.
- ⁇ is a function associated with the corresponding synthesis filter coefficients defined below.
- I' is the identity matrix.
- x be a vector of low frequency wavelet transform coefficients at scale
- FIG. 4 shows low-pass filtered wavelet transform coefficients representative of the low frequency image 12 being passed through a low- frequency synthesis filter 22 corresponding to the low-pass filter 10. Similarly, high-pass filtered wavelet transform coefficients representative of the high-frequency image 18 are passed through a high-frequency synthesis filter 21. The outputs of both synthesis filters 21, 22 are combined at a combining stage 23 to generate the original image 11. However, if some of the high frequency coefficients J+ have been discarded, then x' will lack details that would have been provided by the missing
- Equation (13a) can be written
- G ' is the regularization operator and ⁇ is a positive scalar such that ⁇ - 0 as the accuracy of x /+ increases.
- G ' must be a high-pass filter.
- H' is the low-pass filter matrix of the biorthogonal wavelet transform, G 7 , must be the corresponding high-pass filter matrix.
- Equation (15) may be also written with respect to the estimated wavelet transform coefficients cT and x J+ .
- T refers to the matrix transpose
- a data recovery stage 19 for implementing equations (18) and (19), shown in FIG. 3, provides a way to estimate the high frequency components c' + of the image using only the low frequency components x +1 and matrices derived from the known properties of the two orthogonal filters: G , G , H , and H .
- FIG. 3 shows the wavelet transform coefficients representative of the low-frequency image 12 being passed through a low-frequency synthesis filter 22 as they were in FIG. 4. However, unlike the system of FIG. 4, these same wavelet transform coefficients are used to estimate the high-frequency coefficients representative of the high-frequency image 18. This is accomplished by passing the low-frequency coefficients to an estimation system 28 consisting of an estimation filter 24 followed by a high-frequency synthesis filter 21.
- the matrix M associated with the estimation filter 24 can be precalculated for the selected biorthogonal wavelet set.
- the output of the estimation filter 24 is then filtered by the high-frequency synthesis filter 21.
- the outputs of both synthesis filters 21, 22 are then combined at the combining stage 23 as was the case in FIG. 7.
- the combining stage output represents an estimate of the original image 11a.
- the estimation system 28 of FIG. 3 provides a good initial estimate of c /+ , the missing wavelet transform high-frequency coefficients.
- this estimate can be further refined by an iterative conjugate gradient algorithm using the above initial estimate of c/ and an initial search direction given by the gradient vector VJ ⁇ c , j .
- the search for the global minimum of J is greatly helped by clamping the known values of the vector c ⁇ J .
- FIG. 5 shows a data recovery stage 19 incorporating this clamping function.
- the illustrated data recovery stage 19 is similar to that depicted in FIG. 3 with the exception that the estimation system 28 includes a refinement stage 25 implementing the conjugate gradient method interposed between the estimation filter 24 and the high- frequency synthesis filter 21.
- the refinement stage 25 accepts known values of the high- frequency wavelet transform coefficients 18 and clamps then at those values throughout the iterations of the conjugate gradient algorithm.
- the actual values of the inverse wavelet transform i.e., the x J can be calculated directly without first calculating the J+
- T G ( ⁇ I + G T H- /T H- / G- / ) G- /T H-' T G-'G- /
- FIG. 6 depicts a data recovery stage 19 for implementing the foregoing method.
- the illustrated data recovery stage 19 is similar to that shown in FIG. 3 with the exceptions that the high-pass synthesis filter 21 is no longer necessary and that the estimation filter 26 incorporates the matrix T given in Equation (22). To reduce computation time, T is precalculated for a given biorthogonal wavelet set. In the system of FIG. 6, the output of the estimation filter 26 is passed directly to the combining stage 23.
- the data recovery stage 19 of FIG. 6 is particularly useful for enlarging an image.
- enlarging an image can be accomplished by inserting additional wavelet transform coefficients between known wavelet transform coefficients. The values of these additional wavelet transform coefficients can be estimated using the data recovery stage 19 of FIG. 6.
- FIG. 11 The decompression procedure is illustrated in FIG. 11 for one level of the wavelet transform of data representing an image.
- quadrant a represents the low frequency sub-band and quadrant b and half R represent the higher frequency sub- bands in increasing order.
- FIG. 11 b shows the process of recovering the left side L of a given transform level. If b is empty, i.e., if there are no known high frequency coefficients, matrices T and H are used to compute the columns of L directly, one by one. If the most important coefficients of "6" are known, then matrix M is used to compute an initial estimate of a given column. This estimate is refined by the conjugate gradient method with clamping of the known coefficients to obtain a complete set b ' of high frequency coefficients.
- the inverse wavelet transform on a and b * gives the left side L.
- L and 7? respectively, by rows, we obtain the reproduction of the entire level which is either the low frequency component of the next level or the final decompressed image I if the level is 1, as shown in FIG. l ie.
- This reconstruction process is applied to the luminance and chrominance components the only difference being that no clamping is normally required for the chrominance components since adequate estimates of the high frequency coefficients can be obtained from the low frequency coefficients alone.
- This approach results in higher compression and higher quality reproduced images than any other known method. It will thus be seen that the invention efficiently attains the objects set forth above.
Abstract
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Priority Applications (7)
Application Number | Priority Date | Filing Date | Title |
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RU99116256/09A RU99116256A (en) | 1996-12-20 | 1997-12-12 | METHOD (OPTIONS) AND SYSTEM FOR EVALUATING THE SOURCE SIGNAL |
BR9714419-3A BR9714419A (en) | 1996-12-20 | 1997-12-16 | Enhanced evaluation device to recover high frequency components of compressed image data |
IL13050697A IL130506A0 (en) | 1996-12-20 | 1997-12-16 | Improved estimator for recovering high frequency components from compressed data |
CA002275320A CA2275320A1 (en) | 1996-12-20 | 1997-12-16 | Improved estimator for recovering high frequency components from compressed image data |
AU53794/98A AU5379498A (en) | 1996-12-20 | 1997-12-16 | Improved estimator for recovering high frequency components from compressed ima ge data |
EP97950916A EP0947101A1 (en) | 1996-12-20 | 1997-12-16 | Improved estimator for recovering high frequency components from compressed image data |
JP52881898A JP2001507193A (en) | 1996-12-20 | 1997-12-16 | Improved estimator for recovering high frequency components from compressed data |
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US3370696P | 1996-12-20 | 1996-12-20 | |
US60/033,706 | 1996-12-20 | ||
US6663797P | 1997-11-14 | 1997-11-14 | |
US60/066,637 | 1997-11-14 |
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WO1998028917A1 true WO1998028917A1 (en) | 1998-07-02 |
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EP (1) | EP0947101A1 (en) |
JP (1) | JP2001507193A (en) |
KR (1) | KR20000062277A (en) |
CN (1) | CN1246242A (en) |
AU (1) | AU5379498A (en) |
BR (1) | BR9714419A (en) |
CA (1) | CA2275320A1 (en) |
ID (1) | ID19225A (en) |
IL (1) | IL130506A0 (en) |
RU (1) | RU99116256A (en) |
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WO2000026809A1 (en) * | 1998-10-30 | 2000-05-11 | Caterpillar Inc. | Automatic wavelet generation system and method |
WO2001037575A2 (en) * | 1999-11-18 | 2001-05-25 | Quikcat.Com, Inc. | Method and apparatus for digital image compression using a dynamical system |
WO2001097528A2 (en) * | 2000-06-09 | 2001-12-20 | Hrl Laboratories, Llc | Subband coefficient prediction with pattern recognition techniques |
FR2813001A1 (en) * | 2000-08-11 | 2002-02-15 | Thomson Multimedia Sa | Image display/composition having detection unit copying preceding inter type without residue pixel group or movement compensating using preceding converted image |
US6456744B1 (en) | 1999-12-30 | 2002-09-24 | Quikcat.Com, Inc. | Method and apparatus for video compression using sequential frame cellular automata transforms |
WO2005022463A1 (en) * | 2003-08-28 | 2005-03-10 | Koninklijke Philips Electronics N.V. | Method for spatial up-scaling of video frames |
US7792390B2 (en) * | 2000-12-19 | 2010-09-07 | Altera Corporation | Adaptive transforms |
EP3407604A4 (en) * | 2016-03-09 | 2019-05-15 | Huawei Technologies Co., Ltd. | Method and device for processing high dynamic range image |
US10515440B2 (en) | 2017-08-30 | 2019-12-24 | Samsung Electronics Co., Ltd. | Display apparatus and image processing method thereof |
CN110874581A (en) * | 2019-11-18 | 2020-03-10 | 长春理工大学 | Image fusion method for bioreactor of cell factory |
CN115712154A (en) * | 2022-11-02 | 2023-02-24 | 中国人民解放军92859部队 | Displacement double-wavelet iteration method for detecting shipborne gravity measurement gross error |
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JP4990924B2 (en) * | 2009-01-29 | 2012-08-01 | 日本電信電話株式会社 | Decoding device, encoding / decoding system, decoding method, program |
CN102378011B (en) * | 2010-08-12 | 2014-04-02 | 华为技术有限公司 | Method, device and system for up-sampling image |
US9282328B2 (en) | 2012-02-10 | 2016-03-08 | Broadcom Corporation | Sample adaptive offset (SAO) in accordance with video coding |
US9380320B2 (en) | 2012-02-10 | 2016-06-28 | Broadcom Corporation | Frequency domain sample adaptive offset (SAO) |
CN105427247B (en) * | 2015-11-26 | 2018-08-24 | 努比亚技术有限公司 | A kind of mobile terminal and image processing method of image procossing |
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1997
- 1997-12-12 RU RU99116256/09A patent/RU99116256A/en not_active Application Discontinuation
- 1997-12-16 CA CA002275320A patent/CA2275320A1/en not_active Abandoned
- 1997-12-16 WO PCT/US1997/022685 patent/WO1998028917A1/en not_active Application Discontinuation
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- 1997-12-16 AU AU53794/98A patent/AU5379498A/en not_active Abandoned
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- 1997-12-16 KR KR1019997005668A patent/KR20000062277A/en not_active Application Discontinuation
- 1997-12-22 ID IDP973945A patent/ID19225A/en unknown
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Also Published As
Publication number | Publication date |
---|---|
AU5379498A (en) | 1998-07-17 |
CA2275320A1 (en) | 1998-07-02 |
CN1246242A (en) | 2000-03-01 |
JP2001507193A (en) | 2001-05-29 |
EP0947101A1 (en) | 1999-10-06 |
BR9714419A (en) | 2000-05-02 |
RU99116256A (en) | 2001-05-10 |
IL130506A0 (en) | 2000-06-01 |
ID19225A (en) | 1998-06-28 |
KR20000062277A (en) | 2000-10-25 |
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