ELEC 1020 Media Production: Technology and Design Lecture 3 Prof. James She ([email protected]) 1 Announcement – 1 (Typhoon gone!) 1. Make-up labs for Session 3’s students (+ new students) • • • 6:30–8:30p, Wed. (today, right after the lecture) 4:00–6:00p, Tomorrow same venue - 3119 in Lab. building 2. Lab #2 next Mon. @ 3119 2 Announcement – 2 (Next lecture) Guest Speaker: • Carmen Ng, Creative Lead @ JM Network Topics: • Difference between Design and Art • Outdoor Media Advertising 3 Last lectures 4 What’s Media? Content and Medium Digital Billboard Network Medium 1. Digital display 2. Internet 3.wireless Content 1. REMAX 2. Sales Performance 3. Contact 5 Last lectures 6 Outcomes from this lecture 1. Vision and digital image 2. Digital Image Processing 3. Crowd Accelerated Innovation 7 Image Representation Analog (physical) form - Intensity of light reflected on painting, photograph, film-based negative, etc. 8 Given a digital display How we could see an image like the analog (physical) form? 9 How does your eye works? All about lights intensity… http://www.youtube.com/watch?v=LpjbOhtcD0A&playnext=1&list=PL03E688C16 10 96F94F4&feature=results_main Digital Image Representation Simulating the physicals Amplitude domain: the light intensity at a spatial location represented by a number Spatial domain: a sequence of numbers recorded to represent light intensity at a grid of spatial locations (i.e., pixels on a display) 0 12 100 0 86 255 100 28 255 11 Pixel The basic unit that form an image on a display 0 12 100 0 86 255 100 28 255 12 Pixel The basic unit of an image a pixel • Any digital image on a display is represented by a set of pixels 3 pixels • Pixel not necessary to be a square; Image not necessary to be regular shape • 4 pixels Number of pixels in a given size of image or display defining resolution vs 13 Pixel vs Resolution Given a display area 14 Resolutions & Pixels 15 Resolution vs An image More the better on the same size higher resolution lower resolution 16 Full High Definition (1920 x 1080) 17 Pixel (for B/W or gray-scale image) Define a pixel by light intensity (through # of bits) • A pixel is representing a value of light intensity: 1 bit: “0”=black, “1”=white; 8 bits: “0”=black, “255”=white; • Differences between using? 1) 2-bit and 8-bit per pixel 2) 8-bit and 14-bit per pixel • Which case is worth putting 6 additional bits? • When to invest more bits on a pixel to enrich the digital representation of an image? 18 Bitmap - 1 Bitmap representation (Gray-scale) – An image is formed by pixels – Some computer memory is allocated to each pixel to store the intensity of that pixel – e.g., 3-bit gray scale bitmap representation (how many level?) 6 4 4 3 1 6 1 2 5 2 6 3 1 2 3 2 4 5 1 4 1 6 7 3 5 – Painting tools using bitmap: Photoshop, Microsoft Paint, … 19 Bitmap - 2 Gray scale image – Common natural images: 8 bits – e.g., 8 bit, “0”=black, “255”=white – Medical images: 12 bits – Adjacent pixels are typically similar, except across edges and in texture regions 20 Bitmap - 3 Bitmap representation (color) For each pixel: – 3 primary colors: red, green, blue – 3 color values (3 memory allocations) – 8 bits for each color value • 24 bits or 3 bytes per pixel • 256 x 256 x 256 16,777,216 different colors 21 Bitmap - 4 24-bit RGB bitmap image • Each color component forms a plane • e.g., 150 212 12 23 9 4 11 4 12 3 234 12 1 202 235 3 44 241 5 3 4 32 41 1 2 180 200 3 5 52 2 1 24 1 150 2 13 51 2 4 4 4 2 3 4 35 0 23 191 212 0 113 3 191 41 4 124 5 127 77 1 90 4 87 19 101 Combines to form pixel color 88 125 B plane G plane R plane 22 Outcomes from this lecture 1. Vision and digital image 2. Digital Image Processing 3. Crowd Accelerated Innovation 23 Layers for Image Editing/ Processing 24 Layers in Editing Tools • Layers: like a stack of acetate sheet (i.e., the transparent slide) • Each layer: stores parts of the final image (objects, effects, details, etc.) • Editing operation on pixels across layers could be: 1) independent; 2) interrelated • Editing/processing tools using layers: Photoshop 25 Layers – independent operations 26 Digital Image Processing Alpha channel (recall your lab1) – white acts as the visible area; – black acts as the transparent area – white area: allow you to see the background behind the image when displayed The 8-bit alpha = 255 (i.e., 11111111) The 8-bit alpha = 0 (i.e., 00000000) 27 Digital Image Processing Example use of Alpha channel - 1 28 Digital Image Processing Alpha channel (an operational mask) – 8-bit `gray scale’ alpha channel plane represents the degree of transparency – Level of gray in between determines the level of visibility. e.g., 50% gray 50 percent visibility. – usually used with 16.8M color RGB images. The resulting image is called RGBA (RGB+A, A means alpha channel). i.e., 24-bit color + 8-bit alpha channel 32 bits per pixel The 8-bit alpha = 255 (i.e., 11111111) The 8-bit alpha = 0 (i.e., 00000000) 29 Digital Image Processing Example use of Alpha channel - 2 Image 1 Image 2 a channel i.e., Image 1 x (255 – a)/255+ Image 2 x a/255 30 Layers – interrelated operations layer 1 Gray values layer 2 1 2 7 8 3 4 9 10 Result layer 1 x (255 – a)/255+ layer 2 x a/255 Alpha value (a) 255 128 64 0 7 5 4.5 4 31 Other Practical Color Representations • any other representation beside RGB? 32 Representation of color 33 Other Parameters of Color Representations • Brightness Value (V) – represents brightness of a color, from 0 to 100%. • Hue (H) – represents color, from 0 degrees to 360 degrees. • Saturation (S) – represents the gray-scale of a color space, from 0 to 100%. 34 Brightness Value • 𝑉 = max(𝑅, 𝐺, 𝐵) R: G: B: 32 32 64 H: 240 S: 50% V: 25% 64 64 128 96 96 192 128 128 255 240 50% 50% 240 50% 75% 240 50% 100% 35 Hue Ordering Formula http://www.csgnetwork.com/csgcolorsel4.html R: 64 G: 64 B: 128 R: 64 G: 128 B: 64 36 Hue Ordering Formula http://www.csgnetwork.com/csgcolorsel4.html 37 Saturation –𝑆= 1− min 𝑅,𝐺,𝐵 𝑉 ∗ 100% R: 96 G: 96 B: 128 64 64 128 32 32 128 0 0 128 H: 240 S: 25% V: 50% 240 50% 50% 240 75% 50% 240 100% 50% 38 Image Perception and HSV Contrast The differences of V across pixels • Human eyes: more sensitive to the contrast, rather than absolute colors of pixels in an image • Contrast = the differences of V values across pixels in an image • The bigger the contrast seems looking “nicer”! 40 V values V values V values Photoshop Curves • Adjust Vs for a better perception of an image • Mapping the corresponding RGB values automatically Output Input 1 2 5 8 Output value 1 1 4 8 Input value 41 Photoshop Curves • Improving image by increasing the contrast 42 Value in HSV Value in HSV Photoshop Curves for keying However,… + The range of the image is different = 43 Photoshop Curves • Changing the value to match the background 44 Photoshop Curves for keying Plus other adjustments… + = 45 More practical uses in Photoshop 1. http://www.youtube.com/watch?v=ORM6AZY19pw 2. http://www.youtube.com/watch?v=N-TG0-qpZvs 46 Outcomes from this lecture 1. Vision and digital image 2. Digital Image Processing 3. Crowd Accelerated Innovation 47 Crowd Accelerated Innovation (for arts, knowledge, designs, etc.) PS: Check this if time allows: http://www.youtube.com/watch?v=X6Zo53M0lcY 48 Crowd Accelerated Innovation a. http://www.youtube.com/watch?v=D7o7BrlbaDs&feature=related b. http://www.youtube.com/watch?v=zyLX2cke-Lw&feature=relmfu 49 It also work on images! 50 Crowd Accelerated Mosaic Arts • Combining existing art pieces from others to form a new one from yourself! 51 52 53 54 55 56 Mosaic Arts 57 Crowd Accelerated Innovation 58 Crowd Accelerated Innovation 59 Crowd Accelerated Innovation 60 Crowd Accelerated Innovation 61 - End of Lecture 2 - 62
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