About image compression vs. DCT

Image Compression is a method, which reduces the size of the data to reduce the amount of space required to store the data. The Discrete cosine transform (DCT) is a method for transforms a signal or image from spatial domain to frequency component. It is a widely used technique in image compression.

1. Why is DCT preferred for image compression?
2. What is DCT in image compression?
3. Is DCT lossy or lossless?
4. Why do we use DCT in JPEG image compression algorithm?

Why is DCT preferred for image compression?

The DCT can be used to convert the signal (spatial information) into numeric data ("frequency" or "spectral" information) so that the image's information exists in a quantitative form that can be manipulated for compression. The signal for a graphical image can be thought of as a three-dimensional signal.

What is DCT in image compression?

DCT Definition

The discrete cosine transform (DCT) represents an image as a sum of sinusoids of varying magnitudes and frequencies. The dct2 function computes the two-dimensional discrete cosine transform (DCT) of an image.

Is DCT lossy or lossless?

This allows the DCT technique to be used for lossless compression of images. It is a modification of the original DCT algorithm, and incorporates elements of inverse DCT and delta modulation. It is a more effective lossless compression algorithm than entropy coding. Lossless DCT is also known as LDCT.

Why do we use DCT in JPEG image compression algorithm?

DCT has cosine function which is easier to compute and the number of coefficients become less. Thus, DCT can result more accurate image reconstruction even if the JPEG is lossy transformation. There is one step called quantization where less important pixels are discarded according to the frequency distribution.