bitmapped images-present

8/7/2019 Bitmapped Images-present

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Bitmapped ImagesDigital Multimedia, 2nd edition

Nigel Chapman & Jenny ChapmanChapter 5


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Bitmapped Images

Also known as r aste r g r aphicsRecord 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,


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Image Resolution

Array of pixels has pixel dimensions, but nophysical dimensions

By default, displayed size depends onresolution (dpi) of output device physical dimension = pixel dimension/ r esolution

Can store image r esolution (ppi) in image fileto maintain image's original size

Scale by device r esolution/image r esolution


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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 (disca r d pixels)

Q uality 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 ove r sampled


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Compression

Image files may be too big for networktransmission, even at low resolutionsUse more sophisticated data representation ordiscard information to reduce data sizeEffectiveness of compression will depend onactual image data

For any compression scheme, there willalways be some data for which 'compressed'version is actually bigger than the original


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Lossless Compression

Always possible to decompress compresseddata and obtain an exact copy of the originaluncompressed data

Data is just more efficiently arranged, none isdiscarded

R un-length encoding ( RLE )

Huffmann codingDictiona r y-based schemes LZ77 , LZ78 , LZW (LZW used in GIF, licence fee charged)


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JPEG Compression

Lossy technique, well suited to photog r aphs,images with fine detail and continuous tonesConsider image as a spatially varying signalthat can be analysed in the frequency domainExperimental fact: people do not pe r ceive theeffect of high f r equencies in images ve r y

accu r ately Hence, high frequency information can bediscarded without pe r ceptible loss of quality


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DCT

Similar to Fourier Transform, analyses a signalinto its frequency components

Takes array of pixel values, produces an arrayof coefficients of frequency components in theimageComputationally expensive process timeproportional to square of number of pixels

Apply to 8x8 blocks of pixels


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JPEG Q uantization

Applying DCT does not reduce data size Array of coefficients is same size as array of pixels

Allows information about high frequencycomponents to be identified and discardedUse fewer bits (distinguish fewer differentvalues) for higher frequency components

Number of levels for each frequencycoefficient may be specified separately in aquantization matrix


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JPEG Encoding

After quantization, there will be many zerocoefficients

Use RLE on z ig-zag sequence (maximi zes runs)

Use Huffman coding of other coefficients (bestuse of available bits)


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JPEG Decompression

Expand runs of zeros and decompressHuffman-encoded coefficients to reconstructarray of frequency coefficientsUse Inve r se Disc r ete Cosine T r ansfo r m to takedata back from frequency to spatial domainData discarded in quantization step of

compression procedure cannot be recoveredReconstructed image is an approximation(usually very good) to the original image


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Compression Artefacts

If use low quality setting (i.e. coarserquantization), boundaries between 8x8 blocksbecome visibleIf image has sharp edges these becomeblurred

Rarely a problem with photographic images, butespecially bad with text

Better to use good lossless method with text orcomputer-generated images


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Image Manipulation

Many useful operations described by analogywith 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,


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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


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Masks

Area not selected is p r otected, as if masked by stencil

Can represent on/off mask as array of 1 bit perpixel (b/w image)Generalize to greyscale image(semitransparent mask) alpha channel

Feathered and anti-aliased selections Use as laye r mask to modify laye r compositing


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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 andcontrast Compensate for poor exposure, bad lighting, bring

out detail Use with mask to adjust parts of image

(e.g.shadows and highlights)


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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 histog r am to choose values visually

Curves dialogue Interactively adjust shape of graph of f


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Pixel Group Processing

Compute new value for pixel from its old valueand the values of surrounding pixels

F ilte r ing ope r ations

Compute weighted average of pixel values Array of weights k/a convolution mask Pixels used in convolution k/a convolution ke r nel

Computationally intensive process


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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


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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 originalEnhances image features


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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. inte r polation is r equi r ed

Equivalent to reconstruction & resampling; tends todegrade image quality


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Interpolation

N ea r est neighbou r Use value of pixel whose centre is closest in the

original image to real coordinates of ideal interpolated

pixelB ilinea r inte r polation

Use value of all four adjacent pixels, weighted byintersection with target pixel

B icubic inte r polation Use values of all four adjacent pixels, weighted using

cubic splines

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