Engineering Math Physics (EMP) Digital Cameras Engineering Math Physics (EMP) Jennifer Rexford http://www.cs.princeton.edu/~jrex

Image Transmission Over Wireless Networks Image capture and compression Inner-workings of a digital camera Manipulating & transforming a matrix of pixels Implementing a variant of JPEG compression Wireless networks Wireless technology Acoustic waves and electrical signals Radios Video over wireless networks Video compression and quality Transmitting video over wireless Controlling a car over a radio link

Traditional Photography A chemical process, little changed from 1826 Taken in France on a pewter plate … with 8-hour exposure Taken by Joseph Nicéphore Niépce in France. It is on a pewter plate, and used a petroleum-based compound similar to asphalt as the photographic medium. The parts of the picture exposed to light hardened, and after an eight-hour exposure (!), the areas not exposed to light were washed away with chemicals. There is a very informative website discussing this picture at http://www.hrc.utexas.edu/exhibitions/permanent/wfp/. It’s from the University of Texas at Austin, where this photograph (or “heliograph”, as Niépce called it) currently resides. The world's first photograph

Digital Photography Digital photography is an electronic process Only widely available in the last ten years Digital cameras now surpass film cameras in sales

Image Formation Digital Camera Film Eye

Aperture and Exposure Aperture Shutter speed Diameter of the hole allowing light to enter E.g., the pupil of the eye Higher aperture leads to more light entering … though poorer focus across a wider depth of field Shutter speed Time for light to enter the camera Longer times lead to more light … though blurring of moving subjects Together, determine the exposure The amount of light allowed to enter the camera

Image Formation in a Pinhole Camera Light enters a darkened chamber through pinhole opening and forms an image on the further surface

Image Formation in a Digital Camera Photon +10V + + + + + +        + CCD sensor Array of sensors Light-sensitive diodes that convert photons to electrons Each cell corresponds to a picture element (pixel) Sensor technologies Charge Coupled Device (CCD) Complementary Metal Oxide Semiconductor (CMOS)

Sensor Array: Image Sampling

Sensor Array: Reading Out the Pixels Transfer the charge from one row to the next Transfer charge in the serial register one cell at a time Perform digital to analog conversion one cell at a time Store digital representation Digital-to-analog conversion

Sensor Array: Reading Out the Pixels

More Pixels Mean More Detail Three pictures were taken of the previous scene. Three different resolutions were used. This sample from the center of the picture shows the differences in detail at the four resolutions. 1600 x 1400 is 2.1 megapixels; 1280 x 960 is 1.1 megapixels; 640 x 480 is .3 megapixels, or old Sony Mavica resolution. 640 x 480

The 2272 x 1704 hand The 320 x 240 hand

Representing Color Light receptors in the human eye RGB color model Rods: sensitive in low light, mostly at periphery of eye Cones: only at higher light levels, provide color vision Different types of cones for red, green, and blue RGB color model A color is some combination of red, green, and blue E.g., eight bits for each color With 28 = 256 values Corresponding to intensity Leading to 24 bits per pixel Red: 255, 0, 0 Green: 0, 255, 0 Yellow: 255, 255, 0

Number of Bits Per Pixel More bits can represent a wider range of colors 24 bits can capture 224 = 16,777,216 colors Most humans can distinguish around 10 million colors 8 bits / pixel / color 4 bits / pixel / color

Separate Sensors Per Color Expensive cameras A prism to split the light into three colors Three CCD arrays, one per RGB color

Practical Color Sensing: Bayer Grid Place a small color filter over each sensor Each cell captures intensity of a single color More green pixels, since human eye is better at resolving green

Practical Color Sensing: Interpolating Challenge: estimating pixels we do not know for certain For a non-green cell, look at the neighboring green cells And, interpolate the value Accuracy of interpolation Good in low-contrast areas Poor with sharp edges (e.g., text) Estimate “RGB” at the “G” cells from neighboring values

Digital Images Require a Lot of Storage Three dimensional object Width (e.g., 640 pixels) Height (e.g., 480 pixels) Bits per pixel (e.g., 24-bit color) Storage is the product Pixel width * pixel height * bits/pixel Divided by 8 to convert from bits to bytes Common sizes 640 x 480: 1 Megabyte 800 x 600: 1.5 Megabytes 1600 x 1200: 6 Megabytes

Compression Benefits of reducing the size Redundancy in the image Consume less storage space and network bandwidth Reduce the time to load, store, and transmit the image Redundancy in the image Neighboring pixels often the same, or at least similar E.g., the blue sky Human perception factors Human eye is not sensitive to high frequencies

Contrast Sensitivity Curve

Lossy vs. Lossless Compression Only exploits redundancy in the data So, the data can be reconstructed exactly Necessary for most text documents (e.g., legal documents, computer programs, and books) Lossy Exploits both data redundancy and human perception So, some of the information is lost forever Acceptable for digital audio, images, and video

Examples of Lossless Compression Huffman encoding Assign fewer bits to less-popular symbols E.g., “a” occurs more often than “i” … so encode “a” as “000” and “i” as “00111” Efficient when probabilities vary widely Run-length encoding Identify repeated occurrences of the same symbol Capture the symbol and the number of repetitions E.g., “eeeeeee”  “@e7” E.g., “eeeeetnnnnnn”  “@e5t@n6”

Joint Photographic Experts Group Lossy compression of images Starts with an array of pixels in RGB format With one number per pixel for each of the three colors Outputs a smaller file with some loss in quality Exploits both redundancy and human perception Transforms the data to identify parts that humans notice less More about transforming the data in Wednesday’s class Uncompressed: 167 KB Good quality: 46 KB Poor quality: 9 KB

Conclusion Digital cameras Digital images Image compression Light and a optical lens Charge and electronic devices Pixels and a digital computer Digital images A two-dimensional array of pixels Red, green, and blue intensities for each picture Image compression Raw images are very large Compression reduces the image size substantially By exploiting redundancy and human perception