Source: http://www.google.com/patents/US7317840?dq=5527183
Timestamp: 2016-06-26 20:28:42
Document Index: 394640925

Matched Legal Cases: ['art 01', 'art 1', 'art1', 'art2', 'art3', 'art4']

Patent US7317840 - Methods for real-time software video/audio compression, transmission ... - Google PatentsSearch Images Maps Play YouTube News Gmail Drive More »Sign inPatentsThe invention presents new methods of compression, transmission, and decompression of video signals providing increased speed and image quality. Methods based on wavelet transformation with decimation and time stamping can provide one-pass encoding of signals in which the amount of bits of information...http://www.google.com/patents/US7317840?utm_source=gb-gplus-sharePatent US7317840 - Methods for real-time software video/audio compression, transmission, decompression and displayAdvanced Patent SearchPublication numberUS7317840 B2Publication typeGrantApplication numberUS 10/374,824Publication dateJan 8, 2008Filing dateFeb 25, 2003Priority dateFeb 26, 2002Fee statusPaidAlso published asCA2476904A1, CA2476904C, US20030179941, WO2003073625A2, WO2003073625A3Publication number10374824, 374824, US 7317840 B2, US 7317840B2, US-B2-7317840, US7317840 B2, US7317840B2InventorsAngel DeCegamaOriginal AssigneeDecegama AngelExport CitationBiBTeX, EndNote, RefManPatent Citations (16), Non-Patent Citations (1), Referenced by (17), Classifications (22), Legal Events (9) External Links: USPTO, USPTO Assignment, EspacenetMethods for real-time software video/audio compression, transmission, decompression and display
US 7317840 B2Abstract
The invention presents new methods of compression, transmission, and decompression of video signals providing increased speed and image quality. Methods based on wavelet transformation with decimation and time stamping can provide one-pass encoding of signals in which the amount of bits of information needed to be transmitted can be substantially reduced, thereby increasing the speed of transmission of digital signals over networks. Decompressing signals, along with interpolation methods to re-create portions of images of lesser importance in visual perception, can provide coordinated video and audio presentations of high quality in real-time over all kinds of networks.
1. A system for transmitting audio and video via a communication medium, comprising:
an image capture mechanism operably linked to said video input;
an audio encoder operably linked to said audio input;
a wavelet transform image encoder and decimator operably linked to said image capture mechanism;
an inverse wavelet transform image decoder and interpolator operably linked to said communication medium,
wherein said interpolator uses a computer program to:
receive image data comprising a (j+1)th level vector Xj+1 of low frequency wavelet transform coefficients;
decode and expand said received image data using an inverse wavelet transform, to generate a low-pass filter matrix Hj, a high-pass filter matrix Gj, a high-pass synthesis matrix G j, and a low-pass synthesis matrix H j,
compute an estimation matrix M according to the equation:
M=|αI j + G t j H t j H j G j|−1 G t j H t j G j G j,
in which the subscript “t” refers to the matrix transpose; I is the identity matrix, and where α is a positive scalar such that α→0 as the accuracy of the Xj+1 increases, and
estimate a jth-level vector Xj of wavelet transform coefficients according to the equation:
X j =TX j+1,
where T is the rectangular matrix defined by the equation:
T= H j + G j M; an audio and video synchronizer; and
a receiver for replaying said audio and decoded video images.
2. The system of claim 1, further comprising a coefficient selection mechanism for determining a range of coefficient values between a maximum value per level of wavelet transform, and a minimum value specified as a significance threshold;
said wavelet transform image encoder being a high-frequency coefficient wavelet image encoder.
3. The system of claim 1, further comprising a coefficient selection mechanism for determining a range of coefficient values between a maximum value per level of wavelet transform, and a minimum value specified as a significance threshold;
said wavelet transform image encoder being a high-frequency coefficient wavelet image encoder comprising a coefficient encoder for encoding each coefficient with a number of bits allocated for each range.
4. The system of claim 1, further comprising a coefficient selection mechanism for determining a range of coefficient values between a maximum value per level of wavelet transform, and a minimum value specified as a significance threshold;
said wavelet transform image encoder being a high-frequency coefficient wavelet image encoder comprising a coefficient encoder for encoding each coefficient with a number of bits allocated for each coefficient range and further comprising at least one asymmetrical wavelet filter.
5. The system of claim 1, wherein said decoder comprises:
an image expander for expanding the encoded image along one of a vertical or horizontal axis, to create an interim image that approximates the low frequency side of a level one wavelet transform of the original image; and
an image enhancer, to:
calculate the vertical wavelet transform of the interim image;
enhance local maxima of the wave Let transform; and
replace the coefficient of the result with those of the encoded image.
a communication speed specification mechanism for specifying the transmission speed of the communication medium used by a particular user; and
an expander,
said wavelet transform image encoder using a wavelet transform to match the transmission speed of the communication medium used by that particular user.
7. The system of claim 1, said wavelet transform image encoder comprising at least one asymmetrical filter to differentially decimate low-frequency signals and high-frequency signals.
8. The system of claim 1, wherein said wavelet transform image encoder further comprises:
asymmetrical analysis filters operably linked to said audio input for differentially decimating low-frequency information and high-frequency information in said image; and
a quality threshold input.
a quality threshold input; and
asymmetrical analysis filters operably linked to said video input for differentially decimating low-frequency information and high-frequency information in said image based on said quality threshold input.
10. The system of claim 4, wherein said asymmetrical wavelet filter is a biorthogonal filter.
11. A method of transmitting one of a digital image or a video image via a communication medium, comprising the steps of:
(a) receiving an input stream of video or image data including a digital image comprising a (j+1)th level vector Xj+1 of low frequency wavelet transform coefficients;
(b) capturing an image frame;
(c) compressing and encoding said image frame using a wavelet transform to create an encoded frame;
(d) transmitting said encoded frame via said communication medium to a decoder; and
(e) decoding and expanding said encoded frame using an inverse wavelet transform,
said decoding and expanding step (e) comprising:
(e1) generating a low-pass filter matrix Hj, a high-pass filter matrix Gj, a high-pass synthesis matrix G j, and a low-pass synthesis matrix H j;
(e2) computing an estimation matrix M according to the equation:
M=|αI j + G t j H t j G j|−1 G t j H t j G j G j,
in which the subscript “t” refers to the matrix transpose, I is the identity matrix, and where α is a positive scalar such that α→0 as the accuracy of the Xj+1 increases; and
(e3) estimating a jth-level vector Xj of wavelet transform coefficients according to the equation:
X j =T X j+1,
T= H j + G j M, to create an output stream including a digital image.
said step of receiving comprises receiving a first frame of image data from a sequence of digital images;
said step of capturing comprises making said first frame an anchor frame;
said step of compressing and encoding comprises selecting a first frame position located within said frame at which to fix an anchor frame calculation; and
said step of compressing and encoding further comprises calculating a wavelet transform for said first frame.
said step of receiving comprises receiving a first frame of image data and a second frame of image data from a sequence of digital images;
said step of compressing and encoding comprises selecting a first frame position located within said first frame at which to fix anchor frame calculations, and selecting a second frame position located within said second frame, said second frame position being the same as the position in said first frame; and
said step of compressing and encoding further comprises calculating a wavelet transform for each of said first frame and said second frame.
said step of receiving comprises receiving a first frame of image data and a second frame of image data from a sequence of digital images, and wherein said step of receiving further comprises receiving a quality threshold input specifying an image quality threshold;
said step of compressing and encoding comprises selecting a first frame position located within said first frame at which to fix anchor frame calculations, and selecting a second frame position located within said second frame, said second frame position being the same as the position in said first frame;
said step of compressing and encoding further comprises calculating a wavelet transform for each of said first frame and said second frame; and
said step of decoding and expanding further comprises using a comparator to determine at said selected position whether the wavelet transform of said first frame position and the wavelet transform of said second frame are greater than the quality threshold value.
said step of receiving comprises receiving a first frame of image data and a second frame of image data from a sequence of digital images and wherein said step of receiving further comprises receiving a quality threshold input specifying a video quality threshold value corresponding to the maximum allowed difference between an anchor image frame and a subsequent image frame;
said step of compressing and encoding further comprises selecting a first frame position located within said first frame at which to fix anchor frame calculations, and selecting a second frame position located within said second frame, said second frame position being the same as the position in said first frame;
determining at the selected position whether the difference between said first and second frames is less than the quality threshold value, and if so; then dropping said second frame.
16. The method of claim 11, wherein said step (c) of compressing and encoding further comprises:
(c1) determining, using a coefficient selection mechanism, a range of coefficient values between a maximum value per level of wavelet transform, and a minimum value specified as a significance threshold; and
(c2) encoding each coefficient with a number of bits allocated for each coefficient range.
17. The method of claim 11, wherein said step (c) of compressing and encoding further comprises:
(c1) determining a a set of high frequency coefficients of the wavelet transform of the image;
(c2) determining a maximum value per level of wavelet transform;
(c3) specifying a significance threshold as a minimum value;
(c4) determining a range of coefficient values between said maximum and minimum values;
(c5) allocating a number of data bits to each range; and
(c6) encoding each coefficient with the specified number of bits allocated to each range.
18. The method of claim 11, wherein said step (e) of decoding and expanding further comprises:
(e1) expanding the encoded frame along a vertical axis to create an interim image of the low frequency side of the level one wavelet transform of the original image;
(e2) calculating the vertical wavelet transform of the interim image;
(e3) enhancing the vertical wavelet transform obtained in step (e2) with coefficients of the encoded image; and
(e4) expanding the encoded image along a horizontal axis.
said step of receiving further comprises receiving an input as to the communications speed of the communications medium used, and
said step of compressing and encoding comprises compressing and encoding, using a wavelet transform, a selection of said input stream of video or image data to match the transmission speed of the communication medium used by a particular user.
20. The method of claim 11, wherein said step of compressing and encoding comprises using at least one asymmetrical filter to differentially decimate low-frequency signals and high-frequency signals.
said step of receiving comprises receiving a quality threshold input specifying an image quality threshold; and
said step of compressing and encoding comprises using at least one asymmetrical filter to differentially decimate low-frequency signals and high-frequency signals based on said quality threshold input.
said step of compressing and encoding comprises using at least one asymmetrical filter to differentially decimate low-frequency signals and high-frequency signals based on said quality threshold input; and
said step of decoding and expanding comprises using a comparator to compare said image frame and said decoded output stream.
23. The method of claim 11, wherein said step of capturing an image frame comprises reducing said image frame in size.
24. The method of claim 11, wherein said step (e) of decoding and expanding comprises decoding and expanding at least about thirty images per second.
25. The method of claim 14, wherein said step (c) of compressing and encoding further comprises:
(c3) consulting a lookup table providing said number of non-zero coefficients based on the type of said wavelet transform used by said wavelet transform image encoder.
26. The method of claim 23, wherein said reducing is accomplished by one or more of horizontal decimation and vertical decimation.
27. The method of claim 23, wherein said reducing reduces said image frame size by a factor of at least about four.
28. The method of claim 23, wherein said reducing reduces said image frame size by a factor of at least about two in each dimension.
29. The method of claim 16, wherein said wavelet transform is calculated by creating a matrix T comprising said coefficients.
30. The method of claim 29, wherein for most rows of the matrix T, no more than four coefficient values in each of said rows are above the significance threshold.
31. The method of claim 29, wherein said system is able to decode at least about thirty images per second.
This application claims priority to U.S. provisional application Ser. No.: 60/360,184 filed Feb. 26, 2002, incorporated herein fully by reference.
This invention relates to methods and software programs for compressing, transmitting, decompressing and displaying information. Specifically, this invention relates to compression, transmitting, decompressing and displaying video and audio information over all kinds of networks.
The paper, “Image Data Compression with Selective Preservation of Wavelet Coefficients,” Atsumi Eiji et. al, Visual Communications and Image Processing '95, Taipei, Taiwan, Proceedings of the SPIE, Vol. 2501.1995 describes a method for image compression that is also based on the Wavelet Transform. The main thrust of the paper is in two techniques for deciding which high frequency coefficients to keep to achieve optimum quality for a given level of compression for the decompressed image. No mention is made about what to do when no high frequency coefficients are available.
The paper, “Haar Wavelet Transform with Interband Prediction and its Application to Image Coding,” Kukomi N. et al, Electronics and Communications in Japan, Part III—Fundamental Electronic Science, Vol. 78, No. 4, April 1995, herein incorporated fully by reference, describes another method for image compression that uses the Haar wavelet as the basis for the Wavelet Transform. The Haar wavelet is used because of the simple functional forms used to obtain the low and high frequency WT coefficients, i.e., the sum and the difference divided by 2 of two consecutive pixels. Because of these simple relationships, it is postulated that the high frequency coefficients and the first order derivative of the low frequency coefficients are linearly related with a proportionality variable α. Using this linear function to predict the high frequency coefficients from the low frequency coefficients, the error between the actual and predicted high frequency coefficient values can be obtained and the value of a used is the one that minimizes the mean squared error. Thus, instead of encoding the low and the high frequency coefficients, the method consists of encoding the low frequency coefficients and the error between the predicted and the actual high frequency coefficients which presumably reduces the bit rate somehow. This method cannot work for any other type of wavelet and is therefore of limited value.
The paper, “Sub-band Prediction using Leakage Information in Image Coding,” Vaisey, IEEE Transactions on Communications, Vol 43, No. 2/04, Part 01, February 1995, incorporated herein fully by reference, describes a method for image sub-band coding that attempts to predict the high-pass bands from the low-pass bands and then encodes the error between the predicted and actual high-pass bands which requires fewer bits than encoding the actual high-pass bands. The prediction is done by examining a 3�3 neighborhood around each pixel in a given low frequency band and classifying it into one of 17 groups. The result of the classification is then used to choose a family of 9 high frequency coefficient predictors that depend on the appropriate high-pass band. This method suffers from the basic shortcoming of all vector quantization methods: it is not general enough and thus, cannot provide the flexibility necessary to provide rapid, high-quality compression and decompression that can adapt to the wide variety of images characteristic of current video productions.
Moreover, using the decoding and interpolation methods of this invention, video images can be coordinated with audio signals to produce a “seamless” audiovisual presentation in real time over all kinds of networks, without either audio or visual “gaps.”.
To achieve the goals stated above, the present invention discloses that a decimated WT can advantageously be used. Decimation can result in a number of low frequency coefficients which is one half of the number of original values to be encoded and an equal number of high frequency coefficients for a total equal to the original number of values. Without decimation, as in some prior art methods, the WT results in a number of high and low frequency coefficients which is double the original number of values. However, according to the present invention, the decimated WT can be used for compression by discarding some, or all, as is certain embodiments of the invention, of the high frequency coefficients. As is another teaching of the present invention, the decimated WT can also be a basis for expansion, because a given signal can be thought of as the set of low frequency coefficients of the decimated WT of a signal twice as long. In the case of images the expansion factor is 4 instead of 2.
FIG. 4 is obtained from FIG. 3 by repeating the above process on the columns of FIG. 3. FIG. 4 represents level 1 of the WT of FIG. 2. The upper left corner of FIG. 4 is a lower resolution replica of the original image, containing low-frequency components. The lower left, upper right and lower right portions of FIG. 4 represent high frequency components of the original image. Thus, FIG. 4 represents one complete pass of the image through the WT processing.
Additionally, dropping frames can be used to decrease the total number of bits transmitted. For example, human perception has a property known as “flicker fusion” in which a series of still images shown rapidly enough, give rise to the appearance of motion. For the human visual system, flicker fusion occurs typically at a frequency of about 16 frames/second to about 20 frames/second. Higher quality motion can be achieved using a rate of about 30 frames/second, which is readily interpreted as continuous motion. Thus, if a series of video images is captured at a rate of 30 frames/second, and every second frame is dropped, the effective rate is 15 frames/second, which to many people appears to be continuous motion. However, using the methods of this invention, more frames can be dropped, e.g., 2 of every 3, or 3 of every 4, 4 of every 5 or 5 of every 6. By dropping entire frames, the total numbers of bits needed to be transmitted can be reduced by a factor equal to the ratio of dropped frames to transmitted frames. Thus, if a video compression method compresses video data by 120:1, and if 5 of every 6 frames are dropped, the overall effective compression ratio is 120�6:1 or 720:1. If a compression of 1960:1 is used and 5 of every 6 frames is dropped, the effective compression ratio is 1960�6:1=11,760. It can be readily appreciated that these unprecedented degrees of compression can permit very rapid transmission of video signals over all kinds of networks. Dropping frames can be likened to the temporal equivalent to a spatial frame size reduction. As in the case of spatial data reduction, the temporal data reduction can help with the level of video compression, but if the perceived video quality suffers at the receiving end, it is not acceptable. The ability to interpolate, with high quality and speed, between decompressed anchor frames at the receiving end is another novel aspect of this invention. State of the art video interpolation methods use algorithms that are too complex for real-time software implementation.
The methods of this invention can be used in conjunction with error detection and correction techniques such as file metacontent of Digital Fountain Corporation described in “A Scalable and Reliable Paradigm for Media on Demand”, G. B. Horn, P. Kundsgaard, S. B. Lassen, M. Luby, J. F. Rasmussen, IEEE Computer, September 2001, incorporated herein fully by reference. Such error detection and correction methods can provide increased reliablility of transmission (in some cases of 100%) with reduced overhead (in some cases of only 5%).
High frequence (HF) coefficients of the first level of the WT of a given frame can be compared to those of a previous frame according to the following logic: A flow chart describing this process is presented in FIG. 1B.
1. Set count to 0; 2. For all HF coefficients do; 3. D=Difference with corresponding coefficient in same position of anchor fre; 4. If D>threshold, then count=count+1; 5. Go to 2 6. If count>N (allowed maximum number of changes for dropping frames that can be easily interpolated later) then porceed with calculation of WT and its encoding. Make this frame the new anchor frame; 7. Else drop the frame and proceed to process a new frame. C. Encoding WT Coefficients
At this point, local maxima of F can be enhanced (boosted or scaled) to improve the final image quality. Since there is a trade-off involved between reconstruction speed and reconstruction quality, this additional enhancement step maybe omitted if there is insufficient CPU computational power.
An error function Ej (xi) is chosen such that when added to the perturbed coefficients, the resulting output of WT coefficients satisfies two conditions: a) at the rows and columns corresponding to the local maxima, the original local maxima of the WT coefficients are obtained and b) the sum of the differences between the enhanced and original WT coefficients and the rate of change of such differences is minimized.
∫ X i X i + 1 { [ E j ( x ) ] 2 + 2 2 j [ ⅆ ⅆ x E j ( x ) ] 2 ⅆ x where the second term of the integrand is included to prevent spurious local maxima-from distorting the solution.
E j ( x ) - α ⅇ ( x 2 j ) + β ⅇ - ( x 2 j ) The constants α and β are then chosen to satisfy the boundary conditions imposed by condition a) at xi and xi+1.
α = E j ( x i ) ⅇ - X i + 1 / 2 j - E j ( x i + 1 ) ⅇ - X i / 2 j ⅇ ( x i - x i + 1 ) / 2 j - ⅇ ( x i + 1 - x i ) / 2 j ; β−[E j(x i)−αe xi/2 j ]e xi/2 j The above formulas provide a fast and effective method for modifying the decoded WT coefficients prior to applying the standard IWT algorithm. After the IWT is performed, an enhanced version of the decompressed frame is obtained. Experiments have verified the speed and effectiveness of this processing step of the invention.
a n j(f)=<Ψn j , f> (3)
φn j(x)=2−j/2φ(2−j x−n (4)
a n j(f)=< Ψ n j, f> (5)
g 1=(−1)1 h — 1+1, (7b)
g n=(−1)n h −n+1 or g n=(−1)n+1 h −n+1 (biothogonal) (9b)
∑ n h n h _ n + 2 k = δ k , o ( delta function ) ( 9 c ) where hn and gn represent the low-pass analysis filter and the high-pass analysis filter respectively, and h n and g n represent the corresponding synthesis filters.
We now turn to a matrix modified formulation of the one-dimensional wavelet transform. Using the above impulse responses hn and gn, we can define the circular convolution operators at resolution 2j: Hj, Gj, H j, G j. These four matrices are circulant and symmetric. The Hj matrices are built from the hn filter coefficients and similarly for Gj (from gn), H j (from h n) and G j (from g n).
 X _ j + 1 C — x j + 1  =  H j O O G j  �  X _ j X _ j  ( 11 ) where X j+1 is the smoothed vector obtained from X j. The wavelet coefficients C x j+1 contain information lost in the transition between the low frequency bands of scales 2−j and 2−(j+1).
H j X j+1 = H j H j X j (13a)
X j+1 =H j X j (14).
J( X j,α)=| X j+1 −H j X j|2 +α|G j X j|2 where Gj is the regularization operator and α is a positive scalar such that α→0 as the accuracy of X j+1 increases.
J( X j, α)=| X j+1 −X j+1|2 +α|C x j+1|2.
{circumflex over (X)} (j+1) =H j X j =H j( H j X (j+1) G j C x (j+1) (16b)
(keep in mind that X j is estimated.)
Then subtracting(16b) from (16a) gives:
X j+1 −{circumflex over (X)} j+1 =G j G j X j+1 −H j G j C x (j+1) (16a)
J( C x (j+1), α)=|G j G j X j+1 −H j G j C x (j+1)|2 +α|C x j+1|2. (17)
X j =TX j+1 (20)
where T is the matrix
One can appreciate that, since we are dealing with a decimated Wavelet Transform, the matrix T is not square, but rather, it is rectangular. Its dimensions are n�n/2 where n is the size of the data before any given level of transformation. This can be verified from the following sizes for the Wavelet Transform matrices: H and G are n/2�n matrices and H and G are n�n/2. Notice that αI+ G tHtH G is a square matrix of size n/2�n/2 and is invertible if α>o for all wavelet filters.
For example, for a Daubechies—6 wavelet, the two filters that make up the matrix T are
TABLE 1 Expansion Filters Lengths Daubechies - 4 2 Daubechies - 6 3 Daubechies - 8 4 Biorthogonal 3-4 Asymmetrical 2 It can be appreciated that better expansion quality can be obtained using longer filters, whereas naturally shorter filters can provide faster expansion.
The use of wavelet transformation with decimation permits compressing, transmitting and decompressing information with greater speed and quality than currently available methods. The methods of this invention find application in video and/or video/audio digital transmission in network based industries.
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for synchronizing audio and video streamsUS20110054798 *May 1, 2009Mar 3, 2011Inguran, LlcFlow cytometer remote monitoring systemUS20110235712 *Sep 29, 2011Jian-Liang LinLow complexity video decoderUS20150063103 *Sep 4, 2013Mar 5, 2015Nvidia CorporationBandwidth-dependent compressor for robust header compression and method of use thereof* Cited by examinerClassifications U.S. Classification382/240, 375/E07.072, 375/E07.074, 375/E07.254, 375/E07.044, 375/E07.252International ClassificationH04N19/63, H04N19/132, H04N21/63Cooperative ClassificationH04N19/63, H04N19/132, H04N19/70, H04N19/587, H04N19/59, H04N19/635, H04N19/647European ClassificationH04N19/00Q4B2, H04N7/46T2, H04N7/46S, H04N7/26H30M, H04N7/26H30D1, H04N7/26H30H6Legal EventsDateCodeEventDescriptionMay 19, 2003ASAssignmentOwner name: FAST VU CORPORATION, MASSACHUSETTSFree format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNOR:DECEGAMA, ANGEL, PH.D.;REEL/FRAME:014075/0083Effective date: 20030505Jul 14, 2004ASAssignmentOwner 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