COMP135/COMP535 Digital Multimedia, 2nd edition Nigel Chapman & Jenny Chapman Chapter 5 Lecture 5 - Bitmapped Images

COMP135/COMP535 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

COMP135/COMP535 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

COMP135/COMP535 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

COMP135/COMP535 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

COMP135/COMP535 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

COMP135/COMP535 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

COMP135/COMP535 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

COMP135/COMP535 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

COMP135/COMP535 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

COMP135/COMP535 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

COMP135/COMP535 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

COMP135/COMP535 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

COMP135/COMP535 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

COMP135/COMP535 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

COMP135/COMP535 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

COMP135/COMP535 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

COMP135/COMP535 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

COMP135/COMP535 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

COMP135/COMP535 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

COMP135/COMP535 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

COMP135/COMP535 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

COMP135/COMP535 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