digital multimedia, 2nd edition nigel chapman & jenny chapman chapter 5 this presentation ©...

Digital Multimedia, 2nd editionNigel Chapman & Jenny Chapman

Chapter 5

This presentation © 2004, MacAvon Media Productions

Bitmapped Images

© 2004, MacAvon Media Productions

5

• 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, …

Bitmapped Images

118


5

• 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

Device Resolution

118–119


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• 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

Image Resolution

120


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• 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

Changing Resolution

120–122


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• 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

Compression

122–123


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• 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)

Lossless Compression

122–125


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• 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

JPEG Compression

125–126


5

• 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

DCT

125–127


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• 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

JPEG – Quantization

127


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• 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)

JPEG – Encoding

127


5

• 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

JPEG – Decompression

128


5

• 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

Compression Artefacts

129


5

• 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,…

Image Manipulation

130


5

• 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

Selection

131–132


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• 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

Masks

132–135


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• 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)

Pixel Point Processing

136


5

• 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

Adjustments in Photoshop

136–139


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• 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

Pixel Group Processing

139–142


5

• 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

Blurring

142–144


5

• 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

Sharpening

144–147


5

• 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

Geometrical Transformations

148–150


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• 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

Interpolation

150–151

digital multimedia, 2nd edition nigel chapman & jenny chapman chapter 5 this presentation ©...

Documents

higher resolution

analogues of natural

high frequency information

intended output device

actual image datafor

continuous tonesconsider

high frequency components

images original sizescale