The Properties and Fast Algorithm of Quaternion Linear Canonical Transform

The quaternion linear canonical transform (QLCT) is defined in this paper, with proofs given for its reversibility property, its linear property, its odd-even invariant property and additivity property. Meanwhile, the quaternion convolution (QCV), quaternion correlation (QCR) and product theorem of LCT are deduced. Their physical interpretation is given as classical convolution, correlation and product theorem. Moreover, the fast algorithm of QLCT (FQLCT) is obtained, whose calculation complexity for different signals is similar to FFT. In addition, the paper presents the relationship between the convolution and correlation in LCT domains, and the convolution and correlation can be calculated via product theorem in Fourier transform domain using FFT.


Introduction
The linear canonical transform (LCT) is a new tool that comes into being in signal processing [1]- [32].The LCT is the generalization of the FRFT and so on [2] [3] [12].Up till now there have been a lot of papers involving the FRFT and the LCT, such as papers [1]- [10].However, none of them has involved the LCT of quaternion signals (or Hyper-complex signals) even if there has been similar work on FRFT [5].Quaternion signals can be taken as the generalization of scalar, complex signals and vector, and after the introduction of quaternion signals by Hamilton in 1843 [11] it has become one basic tool for multi-channel and multi-dimensional space.For example, grey image [30] can be taken as scalar, and the analytic signal after Hilbert transformation [12] [13] [14] [15] [16] [29]

Definitions of QLCT
For convenience of discussion, we first give some notations used in the following of this paper.( ) is the 1D LCT of ( ) F is classical quaternion Fourier transform operator, and ( ) Fourier transform of ( ) , f x y ; I is equivalence operator; P is odd-even operator; "*" is classical convolution operator; " − " is conjugation operator."N" is integer set; "R" is real set.Define the product operator of two LCTs' transform parameter systems: ( ) ( ) ( ) q q iq jq kq where , , , r i j k q q q q R ∈ , , , i j k are three imaginary units, which satisfy the fol- lowing relations: q iq jq kq = + + is called vector, and r q is called scalar.a q and b q are complex signals.Since the sequences of i, j and k will affect the result, the definition of QLCT would take them into account.
Definition 1: For any quaternion signal where, ( ) ( ) . Meanwhile, in the following of this paper we assume The reversibility transform is defined as where, ( ) ( ) , , , Fourier transform of ( ) , f x y for variable y; if

(
) ( ) ( ) , definition 1 is equivalence transform of ( ) , f x y .As shown above, definition 1 is the generalization of the fractional qu- aternion Fourier transform and the quaternion Fourier transform [18] [19] [20] [21].The reversibility (or reconstruction) is one important property for one transform, especially for the processing in another domain.The following gives the proof of the reversibility property.
Theorem 1: One quaternion ( ) , f x y can be reconstructed from ( ) Proof: The proof is trivial and omitted here.

The Properties of QLCT
In the following section we list the properties and present the proof.Property 1: For any one quaternion signal ( )( ) , n f x y n∈ℵ , the following re- lationship is true: , , n a ∈ℜ .Proof: Since QLCT is one linear transform, property 1 can be easily obtained from definition 1.
Property 2: . Proof: For any one quaternion signal ( ) For 1D signal the right formula is true [2]: The result can be obtained similarly: From (5) (6): Property 3: Proof: This property can be obtained from property 2.
Property 4: If , and insert them into (2): , e e e , e e d d e Let , s x z y = − = − , and substitute them in ( 7): ( ) It can be obtained as well: , , , , We can draw the conclusion that transformed signal of the odd is odd, and even is even. , QLCT doesn't satisfy Parseval's principle.Meanwhile, it is hard to find one obvious relationship between QLCT and Wigner-Ville time-frequency plane.
Some other properties [2] cannot find physical interpretation in QLCT domains.

FRQCV and FRQCR
Convolution and correlation play an important role in signal processing, especially for linear system design and filter design, etc.The convolution in time domain is to the product in Fourier transform domain, that is to say, the classical convolution in time domain can be implemented in Fourier transform domain via FFT, which is beneficial for real-time engineering use.In classical time-frequency analysis correlation is special convolution in that the original signals are implemented via conjugation and so on.This is very important for engineering use [17] [20] [24].The key to this paper is to discover the relationships in fractional quaternion Fourier transform domain between them so that we can find the physical interpretation as that of the classical Fourier transform.
Paper [26] yielded fractional convolution and product theorem for 1D signals first, however, it didn't give the similar physical interpretation as that of the classical theorem.Later papers [27] [28] [29] obtained similar result as the classical theorems.However, they are only for 1D signals.In this section the QCV and QCR of the LCT would be discussed, and can be implemented via FFT.

Fractional Convolution and Product Theorem
In the following, four theorems are yielded, and theorem 2 and 3 are suitable for scalar and complex signals, and theorem 4 and 5 are suitable for scalar, complex signals, vector and quaternion signals.
Theorem 2: For any real scalar or complex signal ( ) , f x y and convolution kernel ( ) Proof: From theorem 2 it can be concluded that the convolution of scalar or complex signal is to the product, frequency-modulated by a chirp, of them in linear canonical transform.Theorem 3: For any real scalar or complex signal ( ) , f x y and convolution kernel ( ) , , e 2 π e , , e , , , Proof: Theorem 4: For one given quaternion function )

C y C i x C y C i x C y C g x y f h x y A f x y h x y
, , Proof:

x y h x y f x y f x y j h x y h x y j f x y h x y f x y h x y j
, , e e , , e , , From the linear property of fractional Fourier transfor , , From theorem 4 we draw the conclusion that the convolution of two quaternion signals is to the summation of product of their components, conjugated or odd-even operated, and the product is frequency modulated by chirps.Meanwhile, it must be noted that the orders of i and j in cannot be disordered.( )( ) ) } ( ) Y. Zhang, G. L.

y g x y f x y h x y f x y h x y j f x y h x y j f x y h x y
From theorem 3, it can be obtained: { } ( ) { } ( ) From the linear property of Fourier transform: ( { } ( ) From theorem 5 we draw the conclusion that, the product, frequency modulated by a chirp, of two quaternion signals is to the summation, amplitude modulated, of their pseudo convolution.

FRQCR
Headings, or heads, are organizational devices that guide the reader through your paper.There are two types: component heads and text heads.
Theorem 6 is suitable for scalar and complex signals, and theorem 7 is suitable for scalar, complex signals, vector and quaternion signals.
Theorem 6: For two scalar (or complex) signals

( )
, f x y and ( ) , and set Proof: the proof is similar with that of FRQCV and is omitted here.
From theorem 6 we draw the conclusion that correlation can be implemented by convolution.Theorem 7: For any two quaternion signals , and let ) Proof: The proof is similar with that of FRQCV and is omitted here.
From theorem 7 we draw the conclusion that the correlation of two quaternion signals is to the summation of convolution of their components, conjugated or odd-even operated.It means that correlation can be implemented by convolution via FFT.

Fast Algorithm of QLCT
Fast algorithm of QLCT is the key to engineering use.The following discusses the efficient implementation in great detail through the decomposition of quaternion [24] and the definition of the QLCT.For one quaternion function ( ) where, ( ) , W u v can be Calculated by two 2D FFT and some scaling transform.The steps of calculating QLCT:  , h x y .The red lines denote the complexity of implementing QCR in time domain directly, and the blue lines denote the complexity of implementing QCR via FFT.For example, Table 1.The calculation complexity of QLCT for different signals.when the size is 60, there is one nearly-ten-times relationship.Moreover, with the increase of size the gap would become bigger and bigger.

Conclusion
One contribution of this paper is that the definition of QLCT is obtained, and its properties are given, and its generalization is proved.The reversibility property disclosed the efficiency of QLCT.The linear property indicated that LCT is linear transform.Another contribution of this paper is that the QCV and QCR of LCT are defined and their relationships and physical interpretation are discovered: the fractional convolution of two quaternion signals is to the summation of product of their components, conjugated or odd-even operated, and the product is frequency modulated by chirps; and the product, frequency modulated by a chirp, of two quaternion signals is to the summation, amplitude modulated, of their pseudo convolution; and the correlation of two quaternion signals is to the summation of convolution of their components, which are conjugated or odd-even operated.The last contribution is that the complexity of QLCT, QCV and QCR are given, and its Fast Algorithm is obtained through implementing them via the product theorem in transformed domain whose complexity is similar to FFT, which is of great importance to engineering use [31] [32].

F
denotes 2D signal in time domain; F is classical Fourier transform operator; in short) is 1DLCT operator, and , L L L a b c d L a b c d L a b c d are also called Hypercomplex signals, which are the generalization of complex signals.Complex signals have two components: the real part and the imaginary part.However, one quaternion signal has four parts, one real component and three imaginary parts:

Theorem 5 :
For any two quaternion signals Journal of Signal and Information Processing

Figure 1 .
Figure 1.The comparison of complexity via FFT and calculation directly.