US RE40081 E1 Abstract This invention introduces a class of multi-band linear phase lapped biorthogonal transforms with fast, VLSI-friendly implementations via lifting steps called the LiftLT. The transform is based on a lattice structure which robustly enforces both linear phase and perfect reconstruction properties. The lattice coefficients are parameterized as a series of lifting steps, providing fast, efficient in-place computation of the transform coefficients as well as the ability to map integers to integers. Our main motivation of the new transform is its application in image and video coding. Comparing to the popular 8×8 DCT, the 8×16 LiftLT only requires 1 more multiplication, 22 more additions, and 6 more shifting operations. However, image coding examples show that the LiftLT is far superior to the DCT in both objective and subjective coding performance. Thanks to properly designed overlapping basis functions, the LiftLT can completely eliminate annoying blocking artifacts. In fact, the novel LiftLT's coding performance consistently surpasses that of the much more complex 9/7-tap biorthogonal wavelet with floating-point coefficients. More importantly, our transform's block-based nature facilitates one-pass sequential block coding, region-of-interest coding/decoding as well as parallel processing.
Claims(39) 1. An apparatus for coding, storing or transmitting, and decoding M×M sized blocks of digitally represented images, where M is an even number, comprising
a. a forward transform comprising
i. a base transform having M channels numbered 0 through M−1, half of said channel numbers being odd and half being even;
ii. an equal normalization factor in each of the M channels selected to be dyadic-rational;
iii. a full-scale butterfly implemented as a series of lifting steps with a first set of dyadic rational coefficients;
iv. M/2 delay lines in the odd numbered channels;
v. a full-scale butterfly implemented as a series of lifting steps with said first set of dyadic rational coefficients; and
vi. a series of lifting steps in the odd numbered channels with a second specifically selected set of dyadic-rational coefficients;
b. means for transmission or storage of the transform output coefficients; and
c. an inverse transform comprising
i. M channels numbered 0 through M−1, half of said channel numbers being odd and half being even;
ii. a series of inverse lifting steps in the odd numbered channels with said second set of specifically selected dyadic-rational coefficients;
iii. a full-scale butterfly implemented as a series of lifting steps with said first set of specifically selected dyadic-rational coefficients;
iv. M/2 delay lines in the even numbered channels;
v. a full-scale butterfly implemented as a series of lifting steps with said first set of specifically selected dyadic-rational coefficients;
vi. an equal denormalization factor in each of the M channels specifically selected to be dyadic-rational; and
vii. a base inverse transform having M channels numbered 0 through M−1.
2. The apparatus of
3. The apparatus of
4. The apparatus of
5. The apparatus of
6. The apparatus of
7. The apparatus of
8. An apparatus for coding, compressing, storing or transmitting, and decoding a block of M×M intensities from a digital image selected by an M×M window moving recursively over the image, comprising:
a. an M×M block transform comprising:
i. an initial stage
ii. a normalizing factor in each channel
b. a cascade comprising a plurality of dyadic rational lifting transforms, each of said plurality of dyadic rational lifting transforms comprising
i. a first bank of pairs of butterfly lifting steps with unitary coefficients between adjacent lines of said transform;
ii. a bank of delay lines in a first group of M/2 alternating lines;
iii. a second bank of butterfly lifting steps with unitary coefficients, and
iv. a bank of pairs of butterfly lifting steps with coefficients of 1/2 between M/2−1 pairs of said M/2 alternating lines;
c. means for transmission or storage of the output coefficients of said M×M block transform; and
d. an inverse transform comprising
i. a cascade comprising a plurality of dyadic rational lifting transforms, each of said plurality of dyadic rational lifting transforms comprising
a) a bank of pairs of butterfly lifting steps with coefficients of 1/2 between said M/2−1 pairs of said M/2 alternating lines;
b) a first bank of pairs of butterfly lifting steps with unitary coefficients between adjacent lines of said transform;
c) a bank of delay lines in a second group of M/2 alternating lines,; and
d) a second bank of pairs of butterfly lifting steps with unitary coefficients between adjacent lines of said transform;
ii. a de-scaling bank,; and
iii. an inverse initial stage.
9. A method of coding, storing or transmitting, and decoding M×M sized blocks of digitally represented images, where M is an even numbera power of
2, comprising
a. transmitting the original picture signals to a coder, which effects the steps of
i. converting the signals with a base transform having M channels numbered 0 through M−1, half of said channel numbers being odd and half being even,;
ii. normalizing the output of the preceding step with a dyadic rational normalization factor in each of said M channels;
iii. processing the output of the preceding step through two lifting steps with a first set of identical dyadic rational coefficients connecting each pair of adjacent numbered channels in a butterfly configuration;
iv. transmitting the resulting coefficients through M/2 delay lines in the odd numbered channels;
v. processing the output of the preceding step through two inverse lifting steps with the first set of dyadic rational coefficients connecting each pair of adjacent numbered channels in a butterfly configuration; and
vi. applying two lifting steps with a second set of identical dyadic rational coefficients connecting each pair of adjacent odd numbered channels to the output of the preceding step;
b. transmitting or storing the transform output coefficients;
c. receiving the transform output coefficients in a decoder; and
d. processing the output coefficients in a decoder, comprising the steps of
i. receiving the coefficients in M channels numbered 0 through M−1, half of said channel numbers being odd and half being even;
ii. applying two inverse lifting steps with dyadic rational coefficients connecting each pair of adjacent odd numbered channels;
iii. applying two lifting steps with dyadic rational coefficients connecting each pair of adjacent numbered channels in a butterfly configuration;
iv. transmitting the result of the preceding step through M/2 delay lines in the even numbered channels;
v. applying two inverse lifting steps with dyadic rational coefficients connecting each pair of adjacent numbered channels in a butterfly configuration;
vi. denormalizing the result of the preceding step with a dyadic rational inverse normalization factor in each of said M channels; and
vii. processing the result of the preceding step through a base inverse transform having M channels numbered 0 through M−1.
10. A method of coding, compressing, storing or transmitting, and decoding a block of M×M intensities from a digital image selected by an M×M window moving recursively over the image, comprising the steps of:
a. Processing the intensities in an M×M block coder comprising the steps of:
i. processing the intensities through an initial stage;
ii. scaling the result of the preceding step in each channel;
b. processing the result of the preceding step through a cascade comprising a plurality of dyadic rational lifting transforms, each of said plurality of dyadic rational lifting transforms comprising
i. a first bank of pairs of butterfly lifting steps with unitary coefficients between adjacent lines of said transform;
ii. a bank of delay lines in a first group of M/2 alternating lines;
iii. a second bank of butterfly lifting steps with unitary coefficients, and
iv. a bank of pairs of butterfly lifting steps with coefficients of 1/2 between M/2−1 pairs of said M/2 alternating lines;
c. transmitting or storing the output coefficients of said M×M block coder;
d. receiving the output coefficients in a decoder; and
e. processing the output coefficients in the decoder, comprising the steps of
i. processing the output coefficients through a cascade comprising a plurality of dyadic rational lifting transforms, each of said plurality of dyadic rational lifting transforms comprising
a) a bank of pairs of butterfly lifting steps with coefficients of 1/2 between said M/2−1 pairs of said M/2 alternating lines;
b) a first bank of pairs of butterfly lifting steps with unitary coefficients between adjacent lines of said transform;
c) a bank of delay lines in a second group of M/2 alternating lines;
d) a second bank of pairs of butterfly lifting steps with unitary coefficients between adjacent lines of said transform;
e) a de-scaling bank; and
f. processing the results of the preceding step in an inverse initial stage.
11. The apparatus of
12. A method of coding, storing or transmitting, and decoding a block of M×M intensities from a digital image selected by an M×M window moving recursively over the image, comprising:
a. processing the intensities in an M×M block coder comprising the steps of: i. processing the intensities through an initial stage; ii. scaling the result of the preceding step in each channel; b. processing the result of the preceding step through a transform coder using a method of processing blocks of samples of digital signals of integer length M comprising processing the digital samples of length M with an invertible linear transform of dimension M, said transform being representable as a cascade, using the steps, in arbitrary order, of: i) at least one +/− 1 butterfly step, ii) at least one lifting step with rational complex coefficients, and iii) at least one scaling factor; c. transmitting or storing the output coefficients of said M×M block coder; d. receiving the output coefficients in a decoder; and e. processing the output coefficients in the decoder into a reconstructed image using the inverse of the coder of steps a. and b. 13. The method of
14. The method of
15. The method of
a) said invertible transform is an approximation of a biorthogonal transform; b) said biorthogonal transformation comprises a representation as a cascade of at least one butterfly step, at least one orthogonal transform, and at least one scaling factor; c) said at least one orthogonal transform comprises a cascade of i) at least one ± 1 butterfly step, ii) at least one planar rotation, and iii) at least one scaling factor; b) said at least one planar rotation being represented by equivalent lifting steps and scale factors; and, c) said approximation is obtained by replacing floating point coefficients in the lifting steps with rational coefficients. 16. The method of
17. The method of
18. The method of
a) said invertible transform is an approximation of a unitary transform; b) said approximation of the unitary transform comprises a representation of the unitary transform as a cascade of at least one butterfly step, at least one orthogonal transform, and at least one scale factor; c) said at least one orthogonal transform being represented as a cascade of ( ) 1 at least one ± 1 butterfly steps, ( ) 2 at least one planar rotation, and ( ) 3 at least one scaling factor; d) said at least one planar rotation being represented by equivalent lifting steps and scale factors; and, e) said approximation being derived by using approximate rational values for the coefficients in the lifting steps. 19. The method of
DCT); discrete Fourier transform (DFT); discrete sine transform (DST).20. The method of
21. The method of
where a, b are selected from the group:
±{
8, 5, 4, 2, 1, 1/2, 1/4, 3/4, 5/4, 1/8, 3/8, 2/5, 5/8, 7/8, 1/16, 3/16, 5/16, 7/16, 9/16, 11/16, 13/16, 15/16, 25/16}.22. The method of
DCT); discrete Fourier transform (DFT); discrete sine transform (DST).23. The method of
4 point DCT is selected from the group of matrices:24. The method of
4 point DCT, 8 point DCT, and 16 point DCT.25. The method of
512 point FFT, 1024 point FFT, 2048 point FFT, and 4096 point FFT.26. A method of coding, storing or transmitting, and decoding sequences of intensities of integer length M recursively selected from a time ordered string of intensities arising from electrical signals, the method comprising the steps of
a) recursively processing the sequences of intensities of integer length M with an invertible forward linear transform of dimension M, said transform being representable as a cascade using the steps, in a preselected arbitrary order, of: ii) at least one ± 1 butterfly step, iii) at least one lifting step with rational complex coefficients, and iv) applying at least one scaling factor; b) compressing the resulting transform coefficients; c) storing or transmitting the compressed transform coefficients; d) receiving or recovering from storage the transmitted or stored compressed transform coefficients; e) decompressing the received or recovered compressed transform coefficients; and f) recursively processing the decompressed transform coefficients with the inverse of the forward linear transform of dimension M, said inverse transform being representable as a cascade using the steps, in the exact reverse order of the preselected arbitrary order, of: ii) at least one inverse butterfly corresponding to each of the at least one ± 1 butterfly step; iii) at least one inverse lifting step corresponding to each of the at least one lifting step with rational complex coefficients; and, iv) applying at least on inverse scaling factor corresponding to the at least one scaling factor. 27. The method of
28. The method of
29. The method of
a) said invertible transform is an approximation of a biorthogonal transform; b) said biorthogonal transformation comprises a representation as a cascade of at least one butterfly step, at least one orthogonal transform, and at least one scaling factor; c) said at least one orthogonal transform comprising a cascade of i) at least one ± 1 butterfly step, ii) at least one planar rotation, and iii) at least one scaling factor; b) said at least one planar rotation being represented by equivalent lifting steps and scale factors; and, c) said approximation being obtained by replacing floating point coefficients in the lifting steps with rational coefficients. 30. The method of
31. The method of
32. The method of
a) said invertible transform is an approximation of a unitary transform; b) said approximation of the unitary transform comprises a representation of the unitary transform as a cascade of at least one butterfly step, at least one orthogonal transform, and at least one scale factor; c) said at least one orthogonal transform being represented as a cascade of ( ) 1 at least one ± 1 butterfly steps, ( ) 2 at least one planar rotation, and ( ) 3 at last one scaling factor; d) e) said approximation being derived by using approximate rational values for the coefficients in the lifting steps. 33. The method of
DCT); discrete Fourier transform (DFT); discrete sine transform (DST).34. The method of
35. The method of
where a, b are selected from the group:
8, 5, 4, 2, 1, 1/2, 1/4, 3/4, 5/4, 1/8, 3/8, 2/5, 5/8, 7/8, 1/16, 3/16, 5/16, 7/16, 9/16, 11/16, 13/16, 15/16, 25/16}.36. The method of
37. The method of
4 point DCT is selected from the group of matrices:38. The method of
4 point DCT, 8 point DCT, and 16 point DCT.39. The method of
512 point FFT, 1024 point FFT, 2048 point FFT, and 4096 point FFT.Description More than one reissue application has been filed for the reissue of U.S. Pat. No. The current invention relates to the processing of images such as photographs, drawings, and other two dimensional displays. It further relates to the processing of such images which are captured in digital format or after they have been converted to or expressed in digital format. This invention further relates to use of novel coding methods to increase the speed and compression ratio for digital image storage and transmission while avoiding introduction of undesirable artifacts into the reconstructed images. In general, image processing is the analysis and manipulation of two-dimensional representations, which can comprise photographs, drawings, paintings, blueprints, x-rays of medical patients, or indeed abstract art or artistic patterns. These images are all two-dimensional arrays of information. Until fairly recently, images have comprised almost exclusively analog displays of analog information, for example, conventional photographs and motion pictures. Even the signals encoding television pictures, notwithstanding that the vertical scan comprises a finite number of lines, are fundamentally analog in nature. Beginning in the early 1960's, images began to be captured or converted and stored as two-dimensional digital data, and digital image processing followed. At first, images were recorded or transmitted in analog form and then converted to digital representation for manipulation on a computer. Currently digital capture and transmission are on their way to dominance, in part because of the advent of charge coupled device (CCD) image recording arrays and in part because of the availability of inexpensive high speed computers to store and manipulate images. An important task of image processing is the correction or enhancement of a particular image. For example, digital enhancement of images of celestial objects taken by space probes has provided substantial scientific information. However, the current invention relates primarily to compression for transmission or storage of digital images and not to enhancement. One of the problems with digital images is that a complete single image frame can require up to several megabytes of storage space or transmission bandwidth. That is, one of today's 3˝ inch floppy discs can hold at best a little more than one gray-scale frame and sometimes substantially less than one whole frame. A full-page color picture, for example, uncompressed, can occupy 30 megabytes of storage space. Storing or transmitting the vast amounts of data which would be required for real-time uncompressed high resolution digital video is technologically daunting and virtually impossible for many important communication channels, such as the telephone line. The transmission of digital images from space probes can take many hours or even days if insufficiently compressed images are involved. Accordingly, there has been a decades long effort to develop methods of extracting from images the information essential to an aesthetically pleasing or scientifically useful picture without degrading the image quality too much and especially without introducing unsightly or confusing artifacts into the image. The basic approach has usually involved some form of coding of picture intensities coupled with quantization. One approach is block coding; another approach, mathematically equivalent with proper phasing, is multiphase filter banks. Frequency based multi-band transforms have long found application in image coding. For instance, the JPEG image compression standard, W. B. Pennebaker and J. L. Mitchell, “JPEG: Still Image Compression Standard,” Van Nostrand Reinhold, 1993, employs the 8×8 discrete cosine transform (DCT) at its transformation stage. At high bit rates, JPEG offers almost lossless reconstructed image quality. However, when more compression is needed, annoying blocking artifacts appear since the DCT bases are short and do not overlap, creating discontinuities at block boundaries. The wavelet transform, on the other hand, with long, varying-length, and overlapping bases, has elegantly solved the blocking problem. However, the transform's computational complexity can be significantly higher than that of the DCT. This complexity gap is partly in terms of the number of arithmetical operations involved, but more importantly, in terms of the memory buffer space required. In particular, some implementations of the wavelet transform require many more operations per output coefficient as well as a large buffer. An interesting alternative to wavelets is the lapped transform, e.g., H. S. Malvar, Signal Processing with Lapped Transforms, Artech House, 1992, where pixels from adjacent blocks are utilized in the calculation of transform coefficients for the working block. The lapped transforms outperform the DCT on two counts: (i) from the analysis viewpoint, they take into account inter-block correlation and hence provide better energy compaction; (ii) from the synthesis viewpoint, their overlapping basis functions decay asymptotically to zero at the ends, reducing blocking discontinuities dramatically. Nevertheless, lapped transforms have not yet been able to supplant the unadorned DCT in international standard coding routines. The principal reason is that the modest improvement in coding performance available up to now has not been sufficient to justify the significant increase in computational complexity. In the prior art, therefore, lapped transforms remained too computationally complex for the benefits they provided. In particular, the previous lapped transformed somewhat reduced but did not eliminate the annoying blocking artifacts. It is therefore an object of the current invention to provide a new transform which is simple and fast enough to replace the bare DCT in international standards, in particular in JPEG and MPEG-like coding standards. It is another object of this invention to provide an image transform which has overlapping basis functions so as to avoid blocking artifacts. It is a further object of this invention to provide a lapped transform which is approximately as fast as, but more efficient for compression than, the bare DCT. It is yet another object of this invention to provide dramatically improved speed and efficiency using a lapped transform with lifting steps in a butterfly structure with dyadic-rational coefficients. It is yet a further object of this invention to provide a transform structure such that for a negligible complexity surplus over the bare DCT a dramatic coding performance gain can be obtained both from a subjective and objective point of view while blocking artifacts are completely eliminated. In the current invention, we use a family of lapped biorthogonal transforms implementing a small number of dyadic-rational lifting steps. The resulting transform, called the LiftLT, not only has high computation speed but is well-suited to implementation via VLSI. Moreover, it also consistently outperforms state-of-the-art wavelet based coding systems in coding performance when the same quantizer and entropy coder are used. The LiftLT is a lapped biorthogonal transform using lifting steps in a modular lattice structure, the result of which is a fast, efficient, and robust encoding system. With only 1 more multiplication (which can also be implemented with shift-and-add operations), 22 more additions, and 4 more delay elements compared to the bare DCT, the LiftLT offers a fast, low-cost approach capable of straightforward VLSI implementation while providing reconstructed images which are high in quality, both objectively and subjectively. Despite its simplicity, the LiftLT provides a significant improvement in reconstructed image quality over the traditional DCT in that blocking is completely eliminated while at medium and high compression ratios ringing artifacts are reasonably contained. The performance of the LiftLT surpasses even that of the well-known 9/7-tap biorthogonal wavelet transform with irrational coefficients. The LiftLT's block-based structure also provides several other advantages: supporting parallel processing mode, facilitating region-of-interest coding and decoding, and processing large images under severe memory constraints. Most generally, the current invention is an apparatus for block coding of windows of digitally represented images comprising a chain of lattices of lapped transforms with dyadic rational lifting steps. More particularly, this invention is a system of electronic devices which codes, stores or transmits, and decodes M×M sized blocks of digitally represented images, where M is an even number. The main block transform structure comprises a transform having M channels numbered 0 through M−1, half of said channel numbers being odd and half being even; a normalizer with a dyadic rational normalization factor in each of said M channels; two lifting steps with a first set of identical dyadic rational coefficients connecting each pair of adjacent numbered channels in a butterfly configuration, M/2 delay lines in the odd numbered channels; two inverse lifting steps with the first set of dyadic rational coefficients connecting each pair of adjacent numbered channels in a butterfly configuration; and two lifting steps with a second set of identical dyadic rational coefficients connecting each pair of adjacent odd numbered channels; means for transmission or storage of the transform output coefficients; and an inverse transform comprising M channels numbered 0 through M−1, half of said channel numbers being odd and half being even; two inverse lifting steps with dyadic rational coefficients connecting each pair of adjacent odd numbered channels; two lifting steps with dyadic rational coefficients connecting each pair of adjacent numbered channels in a butterfly configuration; M/2 delay lines in the even numbered channels; two inverse lifting steps with dyadic rational coefficients connecting each pair of adjacent numbered channels in a butterfly configuration; a denormalizer with a dyadic rational inverse normalization factor in each of said M channels; and a base inverse transform having M channels numbered 0 through M−1. Typically, a block transform for image processing is applied to a block (or window) of, for example, 8×8 group of pixels and the process is iterated over the entire image. A biorthogonal transform in a block coder uses as a decomposition basis a complete set of basis vectors, similar to an orthogonal basis. However, the basis vectors are more general in that they may not be orthogonal to all other basis vectors. The restriction is that there is a “dual” basis to the original biorthogonal basis such that every vector in the original basis has a “dual” vector in the dual basis to which it is orthogonal. The basic idea of combining the concepts of biorthogonality and lapped transforms has already appeared in the prior art. The most general lattice for M-channel linear phase lapped biorthogonal transforms is presented in T. D. Tran, R. de Queiroz, and T. Q. Nguyen, “The generalized lapped biorthogonal transform,” ICASSP, pp. 1441-1444, Seattle, May 1998, and in T. D. Tran, R. L. de Queiroz, and T. Q. Nguyen, “Linear phase perfect reconstruction filter bank: lattice structure, design, and application in image coding” (submitted to EEE Trans. on Signal Processing, April 1998). A signal processing flow diagram of this well-known generalized filter bank is shown in FIG. In the current invention, which we call the Fast LiftLT, we apply lapped transforms based on using fast lifting steps in an M-channel uniform linear-phase perfect reconstruction filter bank, according to the generic polyphase representation of FIG. The transform decomposition expressed by equations (1) through (3) is readily represented, as shown in It is well known that any M/2×M/2 orthogonal matrix can be factorized into M(M−2)/8 plane rotations θ It is also well known that a plane rotation can be performed by 3 “shears”:
Each of the factors above is capable of a “lifting” step in signal processing terminology. The product of two which effects a linear transform of pairs of coefficients:
Combining the foregoing, the shears referred to can be expressed as computationally equivalent “lifting steps” in signal processing. In other words, we can replace each “rotation” by 3 closely-related lifting steps with butterfly structure. It is possible therefore to implement the complete LT lattice shown in In the simplest but currently preferred embodiment, to minimize the complexity of the transform we choose a small overlapping factor K=2 and set the initial stage E Following the observation in H. S. Malvar, “Lapped biorthogonal transforms for transform coding with reduced blocking and ringing artifacts,” ICASSP97, Munich, April 1997, we apply a scaling factor to the first DCT's antisymmetric basis to generate synthesis LT basis functions whose end values decay smoothly to exact zero—a crucial advantage in blocking artifacts elimination. However, instead of scaling the analysis by √{square root over (2)} and the synthesis by After 2 series of ±1 butterflies W and the delay chain Λ(z), the LT symmetric basis functions already have good attenuation, especially at DC (ω=0). Hence, we can comfortably set U As noted, V Here, the free parameters u Most importantly, fast-computable VLSI-friendly transforms are readily available when u The resulting LiftLT lattice structures are presented in The inverse or synthesis lattice is shown in FIG. The LiftLT is sufficiently fast for many applications, especially in hardware, since most of the incrementally added computation comes from the 2 butterflies and the 6 shift-and-add lifting steps. It is faster than the type-I fast LOT described in H. S. Malvar, Signal Processing with Lapped Transforms, Artech House, 1992. Besides its low complexity, the LiftLT possesses many characteristics of a high-performance transform in image compression: (i) it has high energy compaction due to a high coding gain and a low attenuation near DC where most of the image energy is concentrated; (ii) its synthesis basis functions also decay smoothly to zero, resulting in blocking-free reconstructed images. Comparisons of complexity and performance between the LiftLT and other popular transforms are tabulated in Table 1 and Table 2. The LiftLT's performance is already very close to that of the optimal generalized lapped biorthogonal transform, while its complexity is the lowest amongst the transforms except for the DCT. To assess the new method in image coding, we compared images coded and decoded with four different transforms: -
- DCT: 8-channel, 8-tap filters
- Type-I Fast LOT: 8-channel, 16-tap filters
- LiftLT: 8-channel, 16-tap filters
- Wavelet: 9/7-tap biorthogonal.
In this comparison, we use the same SPIHT's quantizer and entropy coder, A. Said and W. A. Pearlman, “A new fast and efficient image coder based on set partitioning in hierarchical trees,” IEEE Trans on Circuits Syst. Video Tech., vol. 6, pp. 243-250, June 1996, for every transform. In the block-transform cases, we use the modified zero-tree structure in T. D. Tran and T. Q. Nguyen, “A lapped transform embedded image coder,” ISCAS, Monterey, May 1998, where each block of transform coefficients is treated analogously to a full wavelet tree and three more levels of decomposition are employed to decorrelate the DC subband further.
Table 1 contains a comparison of the complexity of these four coding systems, comparing numbers of operations needed per 8 transform coefficients:
In such a comparison, the number of multiplication operations dominates the “cost” of the transform in terms of computing resources and time, and number of additions and number of shifts have negligible effect. In this table, it is clear that the fast LiftLT is almost as low as the DCT in complexity and more than twice as efficient as the wavelet transform. Table 2 sets forth a number of different performance measures for each of the four methods:
The fast LiftLT is comparable to the optional 8×16 LT transform in coding gain and stopband attenuation an significantly better than the DCT. Reconstructed images for a standard 512×512 “Barbara” test image at 1:32 compression ratio are shown in
PSNR is an acronym for power signal to noise r-atio and represents the logarithm of the ratio of maximum amplitude squared to the mean square error of the reconstructed signal expressed in decibels (dB). The LiftLT outperforms its block transform relatives for all test images at all bit rates. Comparing to the wavelet transform, the LiftLT is quite competitive on smooth images—about 0.2 dB below on Lena. However, for more complex images such as Goldhill or Barbara, the LiftLT consistently surpasses the 9/7-tap wavelet. The PSNR improvement can reach as high as 1.5 dB. Patent Citations
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