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DCT Coding of Nonrectangularly Sampled Images Emre Giinduzhan, A

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DCT Coding of Nonrectangularly Sampled Images Emre Giinduzhan, A IEEE SIGNAL PROCESSING LETTERS, VOL. 1, NO. 9, SEPTEMBER 1994 131 DCT Coding of Nonrectangularly Sampled Images Emre Giinduzhan, A. Enis Cetin, and A Murat Tekalp ... Abstmct-Discrete cosine transform (DCT) coding is widely used for compression of re.ctangularly sampled images. In this .... ................ letter, we address efficient DCT coding of "rectangularly sam- ... ....... pled images. To eKect, we discuss efecient method for .......... this an -....__ -._. the computation of the DCT on nomctanpb sampling grids .. ..........~--__ using the Smith-normal &"position. Sim- results are provided. .... .......... I. INTRODUCTION ...I.. ...... ... N DIGITAL representation of multidimensional (M-D) .... Isignals, various sampling structures can be used that are usually in the form of a lattice or a union of coset of a N=[; :]=[2-1]r-110312 '1 lattice [ 11. Rectangular sampling grids (orthogonal lattices) are the most commonly used lattice smcture. It is well Fig. 1. Coordinate transformation in a hexagonal lattice. known that the sampling efficiency of e depends on the region of support of the spectrum malog signal [2]r [3]. In still images, a 2-D nonreckuguhr sampling grid is first transformed onto a new coordinate system, where it is may require. a smaller number of sampks'per unit area than rectangularly periodic. Rectangular M-D DCT of the trans- a rectangular grid. In video, interlaced sampling grids (3-D formed sequence is computed in the new coordinates, and the nonrectangular @ids) are widely used for reduced flickering result is transformed back to the original coordinates. A short without increasing the transmission bandwidth. review of the notation and the Smith-normal decomposition Most algorithm currently used for processing and compres- is given in Section 11. In Section 111, an efficient method to sion of nonrectangularly sampled signals do not fully exploit compute the DCT and the inverse DCT in nonrectangular the nature of the sampling structure used. The standard practice grids is described. Finally, in Section IV, simulation results to compress nomtangularly sampled still images by JPEG- are presented for quincunx sampling grids [5]-[ 101. type schemes is to first interpolate the image to a rectangular grid, which usually has twice as many pixels. Altematively, 11. PRELIMINARIES nomtangdarly sampled images can be treated as if they were sampled on a but this wouid cause artificial Let 2(n) be a periodic sequence, i.e. high fresueneies to the DCT reprewntation since horizontal and vertical lines in the image will be distorted. Z(n + Nr) = 2(n) Vn,r E ZD (1) Thus, the latter choice usually results in a poorer compression where the matrix N is a nonsingular integer matrix, and it is efficiency. Likewise in video compression, the frame DCT called the periodicity matrix. The number of samples in one option in MPEG-I1 assumes that the even and the odd fields period for a given periodicity matrix N is unique and given were recorded at the same time instant on a rectangular grid, by IdetNI. The periodicity matrix N is not unique because which may introduce artificial high-frequency components. N = NL represents the same periodicity if L is a unimodular In this paper, we address DCT coding of images sampled (IdetLI = 1) integer matrix [4]. on arbitrary lattices. To this effect, we discuss an efficient Let v1 ...VD be linearly independent real vectors in a method for the computation of the DCT on nonrectangular D- dimensional Euclidean space. A lattice A in is the set of all sampling grids, which is similar to the computation of the M- RD linear combinations of v1 ...VD with integer coefficients D DFI' in nonrectangular grids [2], [4]. The method is based [4]. on the Smith-normal decomposition, that is, diagonalizing the A = {nlvl +n2~2+...+nDvD I ni E 2, i = 1,... ,D}. periodicity matrix of the M-D sequence. The original sequence (2) Manuscript received December 14, 1993; approved May 19, 1994. This The matrix V = [VI v2 . .VD] is called the sampling matrix work was suppotted by TUBh'AK and NATO (900012). The associate editor of the lattice,Awhich is not unique. If E is any unimodular coordinating the review of this letter and approving it for publication was Prof. H. I. Trussell. matrix, then V = VE is also a sampling matrix for the same E. Giindlizhan and A. E. Cetin are with the Electrical and Electronics lattice. However, IdetVl is unique for a given lattice, and it Engineering Department, Bilkent University, Bilkent, Ankara, Turkey. is called the sampling density [l]. A. M. Temp is with the Electrical Engineering Department, University of Rochester, Rochester, NY, 14627, USA. Any nonsingular integer matrix N can be diagonalized by IEEE Log Number 9405349. pre and postmultiplication by unimodular integer matrices E 1070-9908/94$04.00 0 1994 IEEE 132 IEEE SIGNAL PROCESSING LEITERS, VOL. 1, NO. 9, SEPTEMBER 1994 ” 75 - 70 ~ 65 ~ u)%so- 55 - 50 - 45 - Fig. 2. Comparison of the two methods for the Barbara image TABLE I COMPRESSION RESULTS FOR BARBARA IMAGE Compression Ratio MSDE 2.17 1.74 2.06 bpp 3.88 3.46 63.85 5.09 4.70 1.32 bpp 6.07 5.61 55.45 7.05 6.43 7.96 7.18 51.17 9.30 8.12 11.46 9.44 46.39 16.22 11.69 TABLE II COMPRESSION RESULTS FOR LENA IMAGE II I Compression Ratio I MSDE I SNR 11 II 3.09 bPP I 2.59 I 1.70 I 75.49 11 0-9 1.58 bpp 2.94 66.04 Fig. 3. Original (a) and reconstructed (b) Barbara images of size (336 x 560) 1 55: 1 1 1 in a quincunx sampling grid (1.57 bpp, SNR = 58.53). 1.17 bpp 3.69 62.08 0.96 bpp 8.35 4.19 59.87 used for transforming an arbitrary periodic sampling grid to 0.82 bpp a rectangular grid. 0.73 bpp 11.00 nI. COMPUTATION OF DCT IN NONRECTANGULARGRIDS 0.62 bpp 12.82 55.32 The sequence will be first transformed to a new coordinate 0.51 bpp 15.61 53.20 system where the periodicity matrix is diagonal. The DCT 11 0.38 bpp I 21.12 I 7.48 I 49.80 11 will be computed in the rectangular coordinate system, and the result will be transformed back to the original coordinates. Let x(n) be a finite-extent M-D sequence with a support IN. and F, respectively, such that [4], [lo] Let 5(n)denote its periodic extention with a periodicity matrix N, which can always be decomposed as in (3). The sequence N = EAF (3) Z(n) can be reordered using the coordinate transformation n’ = E-ln. (4) where i) A is in Smith-normal form (or elementary divisor form), i.e, A is diagonal and A,, 1A221 . ~ADDI(“I” denotes Let us denote the sequence in the new coordinates by 5’(n’). “divides”), and ii) IdetEl = (detF(= 1. Such a decomposi- It can be shown that Z‘(n‘) is periodic with A in the primed tion, which is called the Smith-normal decomposition, can be coordinates. GUNDOzhAN et al.: DCT CODING OF SAMPLED IMAGES 133 An example of the coordinate transformation is shown in The same images are also coded by interpolating the quin- Fig. 1 in a hexagonal sampling structure (for D = 2), where cunx grid to a denser rectangular grid (with twice as many VI and v2 are two basis vectors, and IN is the fundamental samples). The standard JPEG algorithm is then applied. At period for the periodicity matrix N.The basis vectors become the receiver, the incoming bit stream is decoded to get a el and e2 after the coordinate transformation, and I& is the rectangularly sampled image that is decimated back to the fundamental period for the new periodicity matrix that is quincunx grid. The comparison of the compression results diagonal. using the Barbara image is shown in Fig. 2. It can be The algorithm to compute the DCT of a sequence s(n)can seen that the new procedure proposed in this letter provides be summarized as follows: better results at all coding levels. For example, in the case i. Compute the transformation n‘ = E-ln, and find the of the Barbara image compressed to 1.57 bpp, the SNR of sequence in the new coordinates x’(n’). the compresseddecompressed image with the new method is ii. Compute the rectangular DCT of x’ to obtain X’(k’). 58.53, whereas interpolation to rectangular grid first yields the iii. Compute the back transformation k = FTk’ to get X(k). SNR 55.83. In order to compute the inverse DCT, the steps are reversed: i. Compute the transfomtion k’ = (F-’)*k, and find V. CONCLUSION XI( k’). In this letter, the discrete cosine transform (DCT) is defined ii. Compute the rectangular inverse DCT to get z‘(n‘). on nonrectangular sampling grids, and a DCT-based compres- iii. Find x(n) using n = En’. sion scheme for nonrectangularly sampled images is presented. If IN is the support of x(n), then IN should be mapped We note that the DCT can be defined for all lattices by the into the region I:, = [O,All) x [O,A22) for the DCT (or proper choice of the sampling and periodicity matrices. DFT) computation to be possible. Since DFT uses a periodicity makx of A, IN is always mapped into Ih. In the case of REFERENCES the DCT computation, the periodicity matrix is 2A, and due to this fact, it is not possible to map IN into I:, for all [I] E. Dubois, “The sampling and reconstruction of time-varying imagery choices of the sampling and periodicity matrices.
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