Lecture 5 - Bitmapped Images

1. Lecture 5 - Bitmapped Images • Digital Multimedia, 2nd edition • Nigel Chapman & Jenny Chapman • Chapter 5

2. Bitmapped Images • Also known as raster graphics • Record a value for every pixel in the image • Often created from an external source • Scanner, digital camera, … • Painting programs allow direct creation of images with analogues of natural media, brushes, …

3. Device Resolution • Printers, scanners: specify as dots per unit length, often dots per inch (dpi) • Desktop printer 600dpi, typesetter 1270dpi, scanner 300–3600dpi,… • Video, monitors: specify as pixel dimensions • PAL TV 768x576px, 17" CRT monitor 1024x768px,… • dpi depends on physical size of screen

4. Image Resolution • Array of pixels has pixel dimensions, but no physical dimensions • By default, displayed size depends on resolution (dpi) of output device • physical dimension = pixel dimension/resolution • Can store image resolution (ppi) in image file to maintain image's original size • Scale by device resolution/image resolution

5. Changing Resolution • If image resolution < output device resolution, must interpolate extra pixels • Always leads to loss of quality • If image resolution > output device resolution, must downsample (discard pixels) • Quality will often be better than that of an image at device resolution (uses more information) • Image sampled at a higher resolution than that of intended output device is oversampled

6. Compression • Image files may be too big for network transmission, even at low resolutions • Use more sophisticated data representation or discard information to reduce data size • Effectiveness of compression will depend on actual image data • For any compression scheme, there will always be some data for which 'compressed' version is actually bigger than the original

7. Lossless Compression • Always possible to decompress compressed data and obtain an exact copy of the original uncompressed data • Data is just more efficiently arranged, none is discarded • Run-length encoding (RLE) • Huffmann coding • Dictionary-based schemes – LZ77, LZ78, LZW (LZW used in GIF, licence fee charged)

8. JPEG Compression • Lossy technique, well suited to photographs, images with fine detail and continuous tones • Consider image as a spatially varying signal that can be analysed in the frequency domain • Experimental fact: people do not perceive the effect of high frequencies in images very accurately • Hence, high frequency information can be discarded without perceptible loss of quality

9. DCT • Discrete Cosine Transform • Similar to Fourier Transform, analyses a signal into its frequency components • Takes array of pixel values, produces an array of coefficients of frequency components in the image • Computationally expensive process – time proportional to square of number of pixels • Apply to 8x8 blocks of pixels

10. JPEG – Quantization • Applying DCT does not reduce data size • Array of coefficients is same size as array of pixels • Allows information about high frequency components to be identified and discarded • Use fewer bits (distinguish fewer different values) for higher frequency components • Number of levels for each frequency coefficient may be specified separately in a quantization matrix

11. JPEG – Encoding • After quantization, there will be many zero coefficients • Use RLE on zig-zag sequence (maximizes runs) • Use Huffman coding of other coefficients (best use of available bits)

12. JPEG – Decompression • Expand runs of zeros and decompress Huffman-encoded coefficients to reconstruct array of frequency coefficients • Use Inverse Discrete Cosine Transform to take data back from frequency to spatial domain • Data discarded in quantization step of compression procedure cannot be recovered • Reconstructed image is an approximation (usually very good) to the original image

13. Compression Artefacts • If use low quality setting (i.e. coarser quantization), boundaries between 8x8 blocks become visible • If image has sharp edges these become blurred • Rarely a problem with photographic images, but especially bad with text • Better to use good lossless method with text or computer-generated images

14. Image Manipulation • Many useful operations described by analogy with darkroom techniques for altering photos • Correct deficiencies in image • Remove 'red-eye', enhance contrast,… • Create artificial effects • Filters: stylize, distort,… • Geometrical transformations • Scale (change resolution), rotate,…

15. Selection • No distinct objects (contrast vector graphics) • Selection tools define an area of pixels • Draw selection (pen tool, lasso) • Select regular shape (rectangular, elliptical, 1px marquee tools) • Select on basis of colour/edges (magic wand, magnetic lasso) • Adjustments &c restricted to selected area

16. Masks • Area not selected is protected, as if masked by stencil • Can represent on/off mask as array of 1 bit per pixel (b/w image) • Generalize to greyscale image (semi-transparent mask) – alpha channel • Feathered and anti-aliased selections • Use as layer mask to modify layer compositing

17. Pixel Point Processing • Compute new value for pixel from its old value • p' = f(p), f is a mapping function • In greyscale images, ppp alters brightness and contrast • Compensate for poor exposure, bad lighting, bring out detail • Use with mask to adjust parts of image (e.g. shadows and highlights)

18. Adjustments in Photoshop • Brightness and contrast sliders • Adjust slope and intercept of linear f • Levels dialogue • Adjust endpoints by setting white and black levels • Use image histogram to choose values visually • Curves dialogue • Interactively adjust shape of graph of f

19. Pixel Group Processing • Compute new value for pixel from its old value and the values of surrounding pixels • Filtering operations • Compute weighted average of pixel values • Array of weights k/a convolution mask • Pixels used in convolution k/a convolution kernel • Computationally intensive process

20. Blurring • Classic simple blur • Convolution mask with equal weights • Unnatural effect • Gaussian blur • Convolution mask with coefficients falling off gradually (Gaussian bell curve) • More gentle, can set amount and radius

21. Sharpening • Low frequency filter • 3x3 convolution mask coefficients all equal to -1, except centre = 9 • Produces harsh edges • Unsharp masking • Copy image, apply Gaussian blur to copy, subtract it from original • Enhances image features

22. Geometrical Transformations • Scaling, rotation, etc. • Simple operations in vector graphics • Requires each pixel to be transformed in bitmapped image • Transformations may 'send pixels into gaps' • i.e. interpolation is required • Equivalent to reconstruction & resampling; tends to degrade image quality

23. Interpolation • Nearest neighbour • Use value of pixel whose centre is closest in the original image to real coordinates of ideal interpolated pixel • Bilinear interpolation • Use value of all four adjacent pixels, weighted by intersection with target pixel • Bicubic interpolation • Use values of all four adjacent pixels, weighted using cubic splines