Image Compression Techniques Presented By: Palash RoyChowdhury Sai Krishna Madhavaram Mohammed Abdul Kareem

Introduction: Image Compression Data compression algorithms applied exclusively to images Reduces Cost for storage and transmission Few compression types better suited for specific image types Adjustable parameters to improve compression quality

Introduction: Image Compression Image Compression types Lossless PNG BMP TIFF Lossy JPEG JPEG 2000

Introduction: Image Compression Direct cosine Transform (DCT) and Fast Frequency Transform (FFT) Lossy type Image compression algorithms Joint photographic Experts Group (JPEG) is one of the most widely used lossy type algorithms

Introduction: DCT The transformation of a two-dimensional matrix of pixel values into an equivalent matrix of spatial frequency components Once the equivalent matrix of spatial frequency components, known as coefficients, has been derived then any threshold can be dropped.

Introduction: DCT Our vision can perceive sharp edges better Such regions are considered “High frequency zones” DCT algorithms are useful for separating High and Low frequency zones effectively

Direct cosine Transform (DCT)

Direct cosine Transform (DCT) Pixels in the source image are divided into multiple blocks of 8*8. Each individual pixel position has a bit value between 0 to 255. The purpose of using DCT is essentially to replicate these 8*8 blocks.

Direct cosine Transform (DCT) Greyscale created by using cosine transforms. Waves of different frequencies can be added and averaged to form these respective shades. This process also has centering of the pixel value Cosine Transform is then applied using 8*8 bases of Discrete Cosine Transform.

Direct cosine Transform (DCT)

JPEG STANDARD QUANTIZATION MATRIX Varies based on the Quantization matrix Allows us to filter out coefficients based on frequency Large corporations like Adobe have their own proprietary matrix JPEG STANDARD QUANTIZATION MATRIX

Decompression DCT inverse gives back the shifted block but is centered at zero Adding 128 will recenter the pixel values Combining all the pixel blocks gives us back the compressed image.

Results - DCT

Fast fourier Transform FFT uses Decimation in Time algorithm to reduce the computational time for DFT (Direct fourier transform) computation. Details in the image are provided by high frequency components, but they are very susceptible to noise which causes spurious effects Many filters in image processing are based on FFT

Elapsed time is 3.124373 seconds Elapsed time is 7.295931 seconds Results - FFT For DCT, time taken to compress 5 images with different compression ratio: Elapsed time is 3.124373 seconds For FFT, time taken to compress 3 images with different compression ratio: Elapsed time is 7.295931 seconds

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