Digital Media Dr. Jim Rowan ITEC 2110 Bitmapped Images

Device Resolution Determines how finely the device approximates the continuous phenomenon Is closely related to sampling we discussed earlier Can be expressed a number of different ways –Printers and Scanners? Number of dots per inch Number of pixels per inch –Video? Number of pixels, pixel x and y dimensions

Device Resolution When considering scanners and printers pay attention to the resolution. –The number of dots per inch a printer produces will dictate the print size of the image –This can cause what appears to be a small image to become quite large

Device Resolution vs Printed Size Bigger than you thought? –If the printer has a 72 dpi rating and the image was scanned at 600 dpi, printing the image (unscaled) will result in a large image 600/72 = 8.33 times as large –To scale it to get the original size back you would use a scaling factor of 72/600 or 0.12

Device Resolution vs Printed Size Smaller than you thought?

Device Resolution vs Printed Size Smaller than you thought?

Image Formats The pixel x and y dimensions of the image can be seen as a measure of how much DETAIL is contained in the picture Most encode (put in the header) the resolution of the image in Pixels Per Inch (PPI) Many encode (put in the header) the original size as pixel width and pixel height

Resolution Changes? Resolution Increase… Is image resolution lower than the the output device? –Must scale it up... –Must add pixels... –Requires interpolation between pixels Always results in an APPARENT quality REDUCTION in the image

Here the original 4x4 image is doubled in size to 8x8 by adding pixels

If you double the image size you have to add pixels... But what color do you make the additions? ?

Generally you consider what the colors are that surround the original pixel Mathematically this usually takes the form of matrix operation ?

Resolution Decrease… Is image resolution higher than the output device? –Must discard some pixels... –AKA downsampling Downsampling: A paradox –There are fewer bits since you’re throwing some pixels out –But... subjective quality goes up –How? Downsampling routine can use the tossed-out pixels to modify the remaining pixel Intentionally doing this is called oversampling How to do this? ==>

If you cut the image size in half (8x8 -> 4x4)-> = 48 pixels removed You remove 3/4 of the pixels! What do you do with thrown away pixels? 64 pixels 16 pixels

One answer: throw them away! Here it works... because it is a solid color

Another answer: Use the information in the surrounding pixels to influence the remaining pixel

Convolution Calculations Convolution is the mathematical process that image software (like GIMP or Photoshop) use to do special effects More at the end of this lecture…

Browsers... really bad at downsampling Their image processing is not very sophisticated What are the implications? –Use image processing programs to do downsampling (GIMP, Photoshop) are sophisticated enough to take advantage of the extra information so... Images for WWW should be downsampled before they are used on the web.

Data Compression What we’ve seen so far: –Storing an image as an array of pixels –With color stored as three bytes per pixel –Image file gets BIG fast! How to reduce that? Using a color table reduces the file size of the stored image (as seen before) Other data compression techniques ==>

Consider this image: With no compression... RGB encoding => 64 x 3 = 192 bytes Data Compression 64 pixels

Side Note We’ve been talking about RGB encoding for images… So… How many different colors can you make if using a 24 bit RGB color scheme?

Side Note 2 24 bits ===> 3 bytes How many colors? Hint: It’s like having a numbering system that has 256 different numbers in it so… 256*3 = 16,777,216 different colors

Consider this image: 64 pixels RLE compression... 9RGB6RGB2RGB6RGB2RGB 6RGB2RGB6RGB2RGB6RGB 2RGB6RGB9RGB = 49 bytes Data Compression Run Length Encoding

Run Length Encoding This advantage would be dependent on the CONTENT of the image. Why? Could it result in a larger image? How? Generally, any data compression CAN result in a larger file than using the pixel array storage –Dependent on the image contents

Consider this image: 64 pixels RLE compression... 1RGB1RGB1RGB1RGB1RGB... 1RGB1RGB1RGB -> 256 bytes (a tiny lie!) RGBRGBRGB... RGBRGB -> 192 bytes Run Length Encoding: Always better than RGB?

Run Length Encoding RLE is Lossless What is lossless? compressed original compression routine Original decompress routine Original Exact duplicate

Dictionary-based (aka Table-based) compression technique (Note: Data compression works on files other than images) Construct a table of strings (colors) found in the file to be compressed Each occurrence in the file of a string (color) found in the table is replaced by a pointer to that occurrence.

Consider this image: RGBRGB==> [ ][ ] [ ].[ ] [ ][ ] [ ][ ] [ ][ ] [ ][ ] ->14 bytes Data Compression Dictionary-based(Table-based) We’ve seen this! 64 pixels

Lossless techniques Can be used on image files Lossy techniques toss some data out -jpeg is a lossy technique Must be used for executable files Why?

JPEG compression Best suited for natural photographs and similar images –Fine details with continuous tone changes High frequency components are associated with abrupt changes in image intensity JPEG takes advantage that humans don’t perceive the effect of high frequencies accurately

JPEG compression... JPEG finds these high frequency components by –treating the image as a matrix –using the Discrete Cosine Transform (DCT) to convert an array of pixels into an array of coefficients DCT is expensive computationally so it the image is broken into 8x8 pixel squares and applied to each of the squares

JPEG compression... DCT does not actually compress the image Allows most of the high frequency components to be discarded because they do not contribute much to the perceptible quality of the image Encodes the frequencies at different quantization levels giving the low frequency components more quantization levels ==>JPEG uses more storage space for the more visible elements of an image

JPEG compression... Lossy Effective for the kinds of images it is intended for ==> 95% reduction in size Allows the control of degree of compression Suffers from artifacts that causes edges to blur... WHY? HMMMmmmm…

Side Note! To make matters worse… The human vision system is very complex –Upside down –Split- left side of eye to right side of brain –Right side of eye to left side of brain –Cones and rods not uniformly distributed –Cones and rods are upside down resulting in blind spots in each eye that we just ignore! Partially responsible for making lossy techniques to work ==>

One reason lossy compression works

Optical Illusions See Additional Class Information: Illusions

Image Manipulation with GIMP Why? –Correct deficiencies (i.e. flash red eye) encapsulated sequence of operations to perform a particular change –Create images that are difficult or impossible to create in nature special effects

Pixel Point Processing Allows adjustment of color in an image Color adjustment, linear –brightness adjusts every pixel brightness up or down –contrast adjusts the RANGE of brightness increasing or reducing the difference between brightest and darkest areas

Remember this dilemma? Rescaling a bitmapped image is called resampling: Two kinds Downsampling Upsampling Different ways to do this that result in different results P111 Nearest Neighbor, bilinear a& bicubic

Pixel Group Processing Final value for a pixel is affected by its neighbors Because the relationship between a pixel and its neighbors provides information about how color or brightness is changing in that region How do you do this? ==> Convolution!

Convolution & Convolution Masks Very expensive computationally –each pixel undergoes many arithmetic operations If you want all the surrounding pixels to equally affect the pixel in question... use a evenly weighted convolution mask

1/9 Convolution mask X Convolution kernel X Using this convolution mask on this convolution kernel the final value of the pixel (2,2) will be: pixel (2,2) = 1/9(1,1) + 1/9(1,2)+ 1/9(1,3) +1/9(2,1) +1/9(2,2) +1/9(2,3) +1/9(3,1) +1/9(3,2) +1/9(3,3)

1/9 Convolution mask X X Using this convolution mask on this convolution kernel the final value of the pixel (3,2) will be: pixel (3,2) = 1/9(1,2) + 1/9(1,3)+ 1/9(1,4) +1/9(2,2) +1/9(2,3) +1/9(2,4) +1/9(3,2) +1/9(3,3) +1/9(3,4)

1/9 Convolution mask X X Using this convolution mask on this convolution kernel the final value of the pixel (4,2) will be: pixel (4,2) = 1/9(1,3) + 1/9(1,4)+ 1/9(1,45) +1/9(2,3) +1/9(2,4) +1/9(2,5) +1/9(3,3) +1/9(3,4) +1/9(3,5)

1/9 Convolution mask X X Using this convolution mask on this convolution kernel the final value of the pixel (5,2) will be: pixel (5,2) = 1/9(1,4) + 1/9(1,5)+ 1/9(1,6) +1/9(2,4) +1/9(2,5) +1/9(2,6) +1/9(3,4) +1/9(3,5) +1/9(3,6)

1/9 0/93/90/9 Using a different Convolution mask... X X X X X Homework: What would be the effect of this mask?

Convolution Calculations Refer to additional information for examples to be worked

Questions?