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Bitmapped Images. Chapter 5 Nigel Chapman & Jenny Chapman. 118. 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, … - PowerPoint PPT Presentation
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Chapter 5Nigel Chapman & Jenny Chapman
Bitmapped Images
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
Most Popular Formats
Dozens of Formats, but 3 that matter
• GIF – Graphics Interchange Format
• Developed by CompuServe 1987
• JPEG - Joint Photographic Experts Group
• Standard 1992
• PNG – Portable Network Graphics
• 1996
5
GIF (.gif)
• Maximum Colors: 8-bit 256
• Great for
• Icons, Logos, and Line Art
•Basic graphics
•Flat Colors
• First format supported by browsers
• RLE: Run length encoding (Lossless Compression) LZ77 Algorithm.
5
JPEG (.jpg)
•Maximum Colors: 24-bit 16.7 million
•Great for
•Hi-resolution Photographs
•Images with subtle color graduation
• Variation of RLE: Compresses groups of pixels into a single unit (Lossy Compression)
5
PNG (.png)
• Maximum Colors: 24-bit 16.7 million
• Great for
•Mixed images: Photos with graphics
•Photos with transparency
• Developed specifically for the web.
• Lossless compression
• Variable transparency
• Can store vector and object info for editing
5
PNG Story
• In the early days of the web…
• There were few graphics tools that supported PNG format
• People forgot about it
• PNG was completely forgotten
• Not supported by many browsers
• Thus, no proliferation of PNG files.
5
PNG Story
• Lossless compression (like GIFs) but superior colors 24-bit OR 8-bit.
• PNG much better for photographic images than GIF
• Better compression for flat color graphics compared to JPEG
• PNG much better for line art, logos, etc than JPEG
• Plus, support for layers and vector data.
5
PNG should replace GIFwith one exception
• GIF excels at compressing flat color images
• But so does 8-bit PNG
• 8-bit PNG format supports variable transparency;
• GIF only supports flat transparency
• But, GIF support embedded animation
• PNG has no animated format
5
PNG will not replace JPG
• JPG excels at compressing photo realistic images (hi-res 3200+ X 2400+)
24-bit PNG is good but not as good
• JPG is not good at flat color compression
• PNG handles flat color much better
• Overall, JPG might be better for very large photos.
• PNG is certainly better for mixed images and medium to low resolution.
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
• 19”-20” LCD monitor 1280x1024px
• dpi depends on physical size of screen
Device Resolution
118–119
5
Device Resolution
• What is the best resolution display you can buy for under $3000?
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
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
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
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
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
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
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
5
• 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 Artifacts
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
5
• 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
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
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
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
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
5
• 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