Gamma transformation is used to adjust image brightness and contrast by decreasing overall brightness and reducing darker area contrast with higher gamma values. Creating a histogram for an RGB image involves plotting the pixel intensity distribution for each color channel. Neighborhood operations like simple average and weighted average filtering can blur an image, while median and gaussian filters reduce noise and preserve edges. High-boost filtering and unsharp masking enhance edges and details while preserving image structure. Altering bitplanes by zeroing them out impacts image intensity levels and contrast, with more significant effects from the most versus least significant bitplane. Zooming via pixel replication can cause pixelation, loss of detail, and artifacts due to enlarged pixels.

1. B A S I C S O F I M A G E A N D V I D E O P R O C E S S I N G
Introduction

2. Gamma transformation
 Output:
 Inference: Gamma transformation is used in image processing to adjust the
brightness or contrast of an image.
 A higher gamma value in gamma transformation typically decreases the overall
brightness and can also reduce the contrast in the darker areas of an image.

3. Plotting Histogram of image
 Output:
 Inference: creating a histogram of an RGB (Red, Green, Blue) image involves
plotting the distribution of pixel intensities for each color channel.

4. Dividing an Image into 4 quadrants to transform the image as
R,B,G,G blocks

5. Operations to achieve image enhancement:
 Contrast Adjustment
 We define the contrast adjustment parameters alpha and beta. The alpha
parameter controls the contrast (1.0 means no change), and the beta
parameter controls the brightness.

6. Histogram Equalization
 Histogram Equalization
 Histogram Equalization is a computer image processing technique used to
improve contrast in images. It accomplishes this by effectively spreading out
the most frequent intensity values, i.e. stretching out the intensity range of the
image.

7. Neighbourhood operations to achieve image enhancement:
 OUTPUT:
 Inference: A simple average filter gives equal weight to all pixels in the
neighborhood, resulting in a more pronounced blurring effect.

8. A weighted average filter (Gaussian filter)
 OUTPUT:
 Inference:
 Gaussian filter, assigns different weights to the pixels in the neighborhood when
computing the new pixel value.
 The weights are typically higher at the center and decrease as you move away from the
center.
 This results in a smoothing effect while preserving more of the important details in the
image.

9. Image to realize high pass, median, gaussian filters
 OUTPUT:
 Median Filter: The median filter is effective at removing salt-and-pepper noise and
other types of impulse noise. It smooths the image while preserving edges. The quality of
the output image depends on the type and amount of noise in the input image. It is often
used for noise reduction.

10. Image to realize high pass, median, gaussian filters
 OUTPUT:
 Gaussian Filter: The Gaussian filter is used for smoothing and blurring an
image. The quality of the output image depends on the standard deviation
parameter (sigma) and the filter size. A smaller sigma and filter size result in
less smoothing, while larger values produce stronger smoothing. The quality of
the output image may be good for noise reduction or smoothing, but it can lead
to loss of fine details.

11. High boost filtering and unsharp masking
 OUTPUT:
 High-Boost Filtering: High-boost filtering enhances the edges and fine details in the
image.
 The quality of the output image depends on the value of 'k.' A higher 'k' value will result in
stronger edge enhancement. However, a very high 'k' value may lead to artifacts and noise
amplification.
 Unsharp Masking: Unsharp masking is a specific case of high-boost filtering where the
Gaussian-blurred version of the image is subtracted from the original image.
 The quality of the output image depends on the value of 'sigma' for Gaussian smoothing.
 A smaller 'sigma' will result in less smoothing, and a larger 'sigma' will produce stronger
smoothing. The unsharp masked image enhances edges and details while preserving the
overall structure of the image.

12. 8 bit image set any of the bitplanes to zero, and
reconstructing the image
 OUTPUT:
 Observations:
 Zeroing the least significant bit plane (e.g., bit_plane_to_zero = 0) will result in
a visually subtle change in the image. It may introduce some quantization-like
artifacts but generally preserves most of the image details.

13. 8 bit image set any of the bitplanes to zero, and
reconstructing the image
 Observations:
 Zeroing the most significant bit plane (e.g., bit_plane_to_zero = 7) will have a
more pronounced impact on the image. This operation will reduce the overall
intensity levels and contrast in the image, making it appear darker and less
detailed.
 The choice of which bit plane to zero out depends on the specific application
and the desired visual effect. The least significant bit plane is generally less
perceptible when altered, while the most significant bit plane has a more
significant impact on the image's appearance.

14. Perform zooming operation through pixel replication and its
challenges
Observations:
 Performing zooming via pixel replication involves duplicating pixels to increase
the image size, which can cause some challenges:
 Pixelation: This method can result in a loss of image quality, causing the
image to appear pixelated or blocky, especially when zooming in significantly.
 Loss of Detail: Duplicating pixels doesn't add new information; it merely
repeats existing data. This can lead to a loss of fine details and sharpness in the
image.
 Artifacts and Blurriness: When the replication factor is too high, the image
might appear blurry or contain artifacts due to the enlarged pixels.

15. Edge detection algorithms

16. Thank You