digital multimedia, 2nd edition nigel chapman & jenny chapman chapter 5 this presentation ©...
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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
© 2004, MacAvon Media Productions
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
© 2004, MacAvon Media Productions
5
• 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
© 2004, MacAvon Media Productions
5
• 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
© 2004, MacAvon Media Productions
5
• 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
© 2004, MacAvon Media Productions
5
• 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
© 2004, MacAvon Media Productions
5
• 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
© 2004, MacAvon Media Productions
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
© 2004, MacAvon Media Productions
5
• 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
© 2004, MacAvon Media Productions
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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
© 2004, MacAvon Media Productions
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
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• 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
© 2004, MacAvon Media Productions
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
© 2004, MacAvon Media Productions
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• 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
© 2004, MacAvon Media Productions
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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
© 2004, MacAvon Media Productions
5
• 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
© 2004, MacAvon Media Productions
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
© 2004, MacAvon Media Productions
5
• 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
© 2004, MacAvon Media Productions
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
© 2004, MacAvon Media Productions
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
© 2004, MacAvon Media Productions
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
© 2004, MacAvon Media Productions
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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
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