 # Discrete Cosine Transform DCT and its Inverse IDCT without using MATLAB Built in Functions

by admin in on March 9, 2021
• • • • • The discrete cosine transform (DCT) represents an image as a sum of sinusoids of varying magnitudes and frequencies. The DCT computes the two-dimensional discrete cosine transform (DCT) of an image. The DCT has the property that, for a typical image, most of the visually significant information about the image is concentrated in just a few coefficients of the DCT. For this reason, the DCT is often used in image compression applications. For example, the DCT is at the heart of the international standard lossy image compression algorithm known as JPEG. (The name comes from the working group that developed the standard: the Joint Photographic Experts Group).
The code works by the following steps:

1. Our first step is to perform the DCT on the whole image, this is done using the following code that calls our dct_2d function. This code first read the image then convert it to a gray scale image. The call the DCT function to do the DCT transformation and presents the results. The results of the DCT on the whole image is as in the following picture, the process of DCT takes long time so we saved the result of DCT in mat file so we can call the result later without performing the DCT. 2. Now in This step we are interested in performing an 8×8 DCT transformation. We used the same DCT function that we already built, but with two For loops of step 8 on rows and columns. The result of applying the 8×8 DCT transformation is as presented in the following picture: 3. Now let’s make the AC components of the 8×8 DCT transformed image equal to zero. The result is as the following: 4. The final step is to recover the DCT converted image in both cases in using DCT on whole image and in using DCT on 8×8 image blocks. The used function is our dct_2d function using For loops (in case of 8×8 blocks). The result is as the following: The two-dimensional DCT of an M-by-N matrix `A` is defined as follows.

```  ```

The values Bpq are called the DCT coefficients of `A`. (Note that matrix indices in MATLAB® always start at 1 rather than 0; therefore, the MATLAB matrix elements `A(1,1)` and `B(1,1)` correspond to the mathematical quantities A00 and B00, respectively.)

The DCT is an invertible transform, and its inverse is given by

```  ```

The inverse DCT equation can be interpreted as meaning that any M-by-N matrix `A` can be written as a sum of MN functions of the form

```  ```

These functions are called the basis functions of the DCT. The DCT coefficients Bpq, then, can be regarded as the weights applied to each basis function. For 8-by-8 matrices, the 64 basis functions are illustrated by this image.

The 64 Basis Functions of an 8-by-8 Matrix

The image down shows a combination of horizontal and vertical frequencies for an 8 x 8 ( ) two-dimensional DCT. Each step from left to right and top to bottom is an increase in frequency by 1/2 cycle. For example, moving the right one from the top-left square yields a half-cycle increase in the horizontal frequency. Another move to the right yields two half-cycles. A move down yields two half-cycles horizontally and a half-cycle vertically. The source data (8×8) is transformed to a linear combination of these 64 frequency squares. Horizontal frequencies increase from left to right, and vertical frequencies increase from top to bottom. The constant-valued basis function at the upper left is often called the DC basis function, and the corresponding DCT coefficient B00 is often called the DC coefficient.

### Applications

The DCT is the most widely used transformation technique in signal processing, and by far the most widely used linear transform in data compression. DCT data compression has been fundamental to the Digital Revolution. Uncompressed digital media, as well as lossless compression, had impractically high memory and bandwidth requirements, which was significantly reduced by the highly efficient DCT lossy compression technique, capable of achieving data compression ratios from 8:1 to 14:1 for near-studio-quality, up to 100:1 for acceptable-quality content. The wide adoption of DCT compression standards led to the emergence and proliferation of digital media technologies, such as digital images, digital photos, digital video, streaming media, digital television, streaming television, video-on-demand (VOD), digital cinema, high-definition video (HD video), and high-definition television (HDTV).

The DCT, and in particular the DCT-II, is often used in signal and image processing, especially for lossy compression, because it has a strong “energy compaction” property: in typical applications, most of the signal information tends to be concentrated in a few low-frequency components of the DCT. For strongly correlated Markov processes, the DCT can approach the compaction efficiency of the Karhunen-Loève transform (which is optimal in the decorrelation sense). As explained below, this stems from the boundary conditions implicit in the cosine functions.

DCTs are also widely employed in solving partial differential equations by spectral methods, where the different variants of the DCT correspond to slightly different even/odd boundary conditions at the two ends of the array.

DCTs are also closely related to Chebyshev polynomials, and fast DCT algorithms (below) are used in Chebyshev approximation of arbitrary functions by series of Chebyshev polynomials, for example in Clenshaw–Curtis quadrature.

The DCT is the coding standard for multimedia telecommunication devices. It is widely used for bit rate reduction, and reducing network bandwidth usage. DCT compression significantly reduces the amount of memory and bandwidth required for digital signals.

### References:

1. Stanković, Radomir S.; Astola, Jaakko T. (2012). “Reminiscences of the Early Work in DCT: Interview with K.R. Rao” (PDF)Reprints from the Early Days of Information Sciences60. Retrieved 13 October 2019.
2. Britanak, Vladimir; Yip, Patrick C.; Rao, K. R. (2010). Discrete Cosine and Sine Transforms: General Properties, Fast Algorithms and Integer Approximations. Elsevier. pp. ix, xiii, 1, 141–304. ISBN 9780080464640.
3. Alikhani, Darya (April 1, 2015). “Beyond resolution: Rosa Menkman’s glitch art”. POSTmatter. Retrieved 19 October 2019.
4. Thomson, Gavin; Shah, Athar (2017). “Introducing HEIF and HEVC” (PDF). Apple Inc. Retrieved 5 August 2019.
5. Ahmed, Nasir; Natarajan, T.; Rao, K. R. (January 1974), “Discrete Cosine Transform” (PDF)IEEE Transactions on ComputersC-23 (1): 90–93, doi:10.1109/T-C.1974.223784
6. Rao, K. R.; Yip, P. (1990), Discrete Cosine Transform: Algorithms, Advantages, Applications, Boston: Academic Press, ISBN 978-0-12-580203-1
7. Barbero, M.; Hofmann, H.; Wells, N. D. (14 November 1991). “DCT source coding and current implementations for HDTV”. EBU Technical Review. European Broadcasting Union (251): 22–33. Retrieved 4 November 2019.
8. Lea, William (1994). “Video on demand: Research Paper 94/68”. House of Commons Library. 9 May 1994. Retrieved 20 September 2019.
9. Ahmed, Nasir (January 1991). “How I Came Up With the Discrete Cosine Transform”. Digital Signal Processing1 (1): 4–5. doi:10.1016/1051-2004(91)90086-Z.
10. “T.81 – Digital compression and coding of continuous-tone still images – Requirements and guidelines” (PDF). CCITT. September 1992. Retrieved 12 July2019.

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