Statistical Features (Mean, Variance, Skewness) — MCQs | Digital Image Processing

50
Score: 0
Attempted: 0/50
Subscribe
1. Who is responsible for measuring the central tendency in a grayscale image?



2. Which feature quantifies the average brightness of an image?



3. Which statistical measure indicates the spread or dispersion of pixel values in an image?



4. What does a high variance in an image indicate?



5. What statistical measure helps in understanding the asymmetry of an image histogram?



6. If an image has a positive skewness, how is its histogram shaped?



7. Which feature is best to analyze the brightness consistency across an image?



8. Which of the following is not a first-order statistical feature?



9. Which statistical feature is used to determine whether an image is biased toward darker or lighter pixels?



10. What does zero skewness represent in an image histogram?



11. What happens to the mean if all pixel values are increased by a constant value?



12. Which statistical measure is affected by outliers the most?



13. Which of the following indicates uniform intensity in an image?



14. In a perfectly symmetrical histogram, skewness is:



15. What is the unit of measurement for mean in image processing?



16. Which statistical feature gives the average of the squared differences from the Mean?



17. What is the relation between standard deviation and variance?



18. Which feature is most suitable for detecting brightness distortion in an image?



19. What is the range of pixel values used to calculate statistical features in an 8-bit image?



20. What does a negative skewness indicate in image intensity distribution?



21. Which of the following features is most sensitive to image contrast?



22. Which of the following is computed using all pixel values in an image?



23. A high positive skewness in an image suggests:



24. Which statistical feature can help in dynamic range compression?



25. What is the most basic statistical feature extracted from an image?



26. Skewness is computed using:



27. If an image has uniform gray levels, which feature will be minimum?



28. Which statistical feature is commonly used in image quality analysis?



29. Which statistical measure does not depend on the order of pixel values?



30. Which of the following features is essential in distinguishing between blurred and sharp images?



31. Skewness helps in understanding the:



32. What is the effect of high variance in medical image analysis?



33. Which statistical measure uses third-order central moments?



34. What does the standard deviation represent in image statistics?



35. Which feature is used to detect uneven lighting in an image?



36. A grayscale image has zero variance. What can be inferred?



37. Which statistical feature is used to find average energy in a signal?



38. What does a high mean value in an image usually indicate?



39. Which statistical measure reflects how much image intensity values deviate from the mean?



40. Which of the following features is not influenced by image histogram shape?



41. Which statistical property would change if brightness increases uniformly across an image?



42. If skewness is negative, the histogram tail is towards:



43. Which statistical feature helps detect image illumination issues?



44. Which of these is most useful in analyzing image brightness variation?



45. Which statistical feature is not affected by uniform noise?



46. Which statistical metric helps in compressing image dynamic range?



47. In image processing, what is the key use of computing the mean?



48. If pixel values are symmetrically distributed, the skewness is:



49. Which feature would most likely indicate if an image is overexposed?



50. Which statistical feature helps distinguish between light and dark areas in an image?



More MCQs on Digital image Processing

  1. Introduction to DIP — MCQs | Digital Image Processing

  2. Human Visual System (HVS) — MCQs | Digital Image Processing

  3. Image Acquisition Devices — MCQs | Digital Image Processing

  4. Image Sampling & Quantization — MCQs | Digital Image Processing

  5. Image Resolution & Bit Depth — MCQs | Digital Image Processing

  6. Basic Image Operations (Negative, Log, Power-law) — MCQs | Digital Image Processing

  7. Histogram Equalization & Specification — MCQs | Digital Image Processing

  8. Contrast Stretching — MCQs | Digital Image Processing

  9. Image Arithmetic (Add, Subtract, Multiply, Divide) — MCQs | Digital Image Processing

  10. Bit-plane Slicing — MCQs | Digital Image Processing

  11. Smoothing Filters (Mean, Gaussian, Median) — MCQs | Digital Image Processing

  12. Sharpening Filters (Laplacian, Gradient) — MCQs | Digital Image Processing

  13. High-Boost Filtering — MCQs | Digital Image Processing

  14. Edge Detection (Sobel, Prewitt, Roberts, Canny, LoG) — MCQs | Digital Image Processing

  15. Fourier Transform (DFT, FFT) — MCQs | Digital Image Processing

  16. Frequency Domain Filtering — MCQs | Digital Image Processing

  17. Low-pass & High-pass Filters — MCQs | Digital Image Processing

  18. Homomorphic Filtering — MCQs | Digital Image Processing

  19. Noise Models (Gaussian, Salt & Pepper, Speckle) — MCQs | Digital Image Processing

  20. Adaptive Filtering — MCQs | Digital Image Processing

  21. Inverse & Wiener Filtering — MCQs | Digital Image Processing

  22. Pseudo-color & True-color Processing — MCQs | Digital Image Processing

  23. Color Space Conversion (RGB ↔ HSV, HSI, YCbCr) — MCQs | Digital Image Processing

  24. Color Image Enhancement — MCQs | Digital Image Processing

  25. Image Segmentation (Thresholding, Otsu, K-means, Region Growing) — MCQs | Digital Image Processing

  26. Edge-based Segmentation — MCQs | Digital Image Processing

  27. Region Splitting and Merging — MCQs | Digital Image Processing

  28. Watershed Algorithm — MCQs | Digital Image Processing

  29. Morphological Operations (Erosion, Dilation, Opening, Closing) — MCQs | Digital Image Processing

  30. Boundary Extraction — MCQs | Digital Image Processing

  31. Skeletonization — MCQs | Digital Image Processing

  32. Connected Components Labeling — MCQs | Digital Image Processing

  33. Texture Analysis (GLCM, LBP, Gabor Filters) — MCQs | Digital Image Processing

  34. Shape Descriptors (Perimeter, Area, Compactness, Eccentricity) — MCQs | Digital Image Processing

  35. Statistical Features (Mean, Variance, Skewness) — MCQs | Digital Image Processing

  36. Principal Component Analysis (PCA) — MCQs | Digital Image Processing

  37. Linear Discriminant Analysis (LDA) — MCQs | Digital Image Processing

  38. Feature Matching (SIFT, SURF, ORB) — MCQs | Digital Image Processing

  39. Image Registration — MCQs | Digital Image Processing

  40. Image Stitching — MCQs | Digital Image Processing

  41. Motion Detection & Optical Flow — MCQs | Digital Image Processing

  42. Background Subtraction — MCQs | Digital Image Processing

  43. Object Detection & Tracking — MCQs | Digital Image Processing

  44. Template Matching — MCQs | Digital Image Processing

  45. Pattern Recognition (KNN, SVM, ANN) — MCQs | Digital Image Processing

  46. Image Classification — MCQs | Digital Image Processing

  47. Image Clustering — MCQs | Digital Image Processing

  48. Image Compression (RLE, Huffman, LZW, JPEG, JPEG2000) — MCQs | Digital Image Processing

  49. Video Compression (MPEG, H.264) — MCQs | Digital Image Processing

  50. Image Fusion (Pixel, Feature, Decision Level) — MCQs | Digital Image Processing

  51. Image Watermarking — MCQs | Digital Image Processing

  52. Steganography — MCQs | Digital Image Processing

  53. Face Detection & Recognition — MCQs | Digital Image Processing

  54. Gesture Recognition — MCQs | Digital Image Processing

  55. 3D Image Processing — MCQs | Digital Image Processing

  56. Stereo Vision & Depth Estimation — MCQs | Digital Image Processing

  57. Medical Image Analysis (CT, MRI, Ultrasound) — MCQs | Digital Image Processing

  58. Remote Sensing Image Processing — MCQs | Digital Image Processing

  59. Satellite Image Enhancement — MCQs | Digital Image Processing

  60. Deep Learning for Image Processing (CNN, GANs, Autoencoders) — MCQs | Digital Image Processing

  61. Image Captioning — MCQs | Digital Image Processing

  62. Semantic & Instance Segmentation (Mask R-CNN, U-Net) — MCQs | Digital Image Processing

  63. Super Resolution (SRCNN, ESRGAN) — MCQs | Digital Image Processing

  64. Image Inpainting — MCQs | Digital Image Processing

  65. Image Style Transfer — MCQs | Digital Image Processing

  66. Real-Time Image Processing — MCQs | Digital Image Processing

  67. Augmented Reality (AR) & Virtual Reality (VR) — MCQs | Digital Image Processing

  68. DIP using MATLAB/OpenCV/Python — MCQs | Digital Image Processing

  69. DIP in IoT & Embedded Systems — MCQs | Digital Image Processing

  70. Ethics & Privacy in Image Processing — MCQs | Digital Image Processing

Computer Science Repeated MCQs Book Download

Related Posts:

Contents Copyrights Reserved By T4Tutorials