_______________________________________________________________________________________________ © 2006 Daniel Vik - 105 Color Images on MSX1.

Slides:



Advertisements
Similar presentations
Digital Color 24-bit Color Indexed Color Image file compression
Advertisements

Information Representation
Basic Spreadsheet Functions Objective Functions are predefined formulas that perform calculations by using specific values, called arguments, in.
Image Data Representations and Standards
Lecture # 20 Image and Data Compression. Data Compression.
A Digital Imaging Primer Nick Dvoracek Instructional Resources Center University of Wisconsin Oshkosh.
Graphics File Formats. 2 Graphics Data n Vector data –Lines –Polygons –Curves n Bitmap data –Array of pixels –Numerical values corresponding to gray-
USER VERIFICATION SYSTEM. Scope Web Interface RGB separation Pervasive.
Images.
5. 1 JPEG “ JPEG ” is Joint Photographic Experts Group. compresses pictures which don't have sharp changes e.g. landscape pictures. May lose some of the.
Roger Cheng (JPEG slides courtesy of Brian Bailey) Spring 2007
SIMS-201 Representing Information in Binary. 2  Overview Chapter 3: The search for an appropriate code Bits as building blocks of information Binary.
1 JPEG Compression CSC361/661 Burg/Wong. 2 Fact about JPEG Compression JPEG stands for Joint Photographic Experts Group JPEG compression is used with.jpg.
Image Compression JPEG. Fact about JPEG Compression JPEG stands for Joint Photographic Experts Group JPEG compression is used with.jpg and can be embedded.
Management Information Systems Lection 06 Archiving information CLARK UNIVERSITY College of Professional and Continuing Education (COPACE)
Data starts with width and height of image Then an array of pixel values (colors) The number of elements in this array is width times height Colors can.
CS559-Computer Graphics Copyright Stephen Chenney Image File Formats How big is the image? –All files in some way store width and height How is the image.
Bitmapped Images. Bitmap Images Today’s Objectives Identify characteristics of bitmap images Resolution, bit depth, color mode, pixels Determine the most.
IE433 CAD/CAM Computer Aided Design and Computer Aided Manufacturing Part-2 CAD Systems Industrial Engineering Department King Saud University.
Digital Images The digital representation of visual information.
Faculty of Sciences and Social Sciences HOPE Website Development Graphics Stewart Blakeway FML 213
CS Spring 2012 CS 414 – Multimedia Systems Design Lecture 8 – JPEG Compression (Part 3) Klara Nahrstedt Spring 2012.
By Meidika Wardana Kristi, NRP  Digital cameras used to take picture of an object requires three sensors to store the red, blue and green color.
Representing Nonnumeric Data Everything is really a number.
Tools for Raster Displays CVGLab Goals of the Chapter To describe pixmaps and useful operations on them. To develop tools for copying, scaling, and rotating.
Graphics/Image Data Types
Klara Nahrstedt Spring 2011
Manipulating contrast/point operations. Examples of point operations: Threshold (demo) Threshold (demo) Invert (demo) Invert (demo) Out[x,y] = max – In[x,y]
Huffman Encoding Veronica Morales.
Images The Science of Images What is an Image on the computer? The Psychology of Images What do we use images for? What effect color has on our mood and.
Page 1 Data Structures in C for Non-Computer Science Majors Kirs and Pflughoeft Basic Data Types Remember when we first talked about the different combinations.
Lecture 4 Pixels, Images and Image Files 1. In this Lecture, you will learn the following concepts: Image files (in particular, the BMP file format) How.
Pixels, Images and Image Files 1 By Dr. HANY ELSALAMONY.
Indiana University Purdue University Fort Wayne Hongli Luo
Still-image compression Moving-image compression and File types.
Multimedia Technology Image Technology Krich Sintanakul Multimedia and Hypermedia.
Communicating Quantitative Information Is a picture worth 1000 words? Digital images. Number bases Standards, Compression Will [your] images last? Homework:
Web graphics Discussion Session August 16, 2000 Discussion Session August 16, 2000.
Rick Parent - CIS681 Background Perception Display Considerations Film and Video, Analog and Digital Technology.
 Video Display Devices Video Display Devices  Cathode-ray tube (CRT) Monitors Cathode-ray tube (CRT) Monitors  Display Technologies Display Technologies.
CS Spring 2014 CS 414 – Multimedia Systems Design Lecture 10 – Compression Basics and JPEG Compression (Part 4) Klara Nahrstedt Spring 2014.
Graphics: Conceptual Model Real Object Human Eye Display Device Graphics System Synthetic Model Synthetic Camera Real Light Synthetic Light Source.
How digital cameras work The Exposure The big difference between traditional film cameras and digital cameras is how they capture the image. Instead of.
Figure ground segregation in video via averaging and color distribution Introduction to Computational and Biological Vision 2013 Dror Zenati.
Ch 6 Color Image processing CS446 Instructor: Nada ALZaben.
DIGITAL IMAGE. Basic Image Concepts An image is a spatial representation of an object An image can be thought of as a function with resulting values of.
A New Operating Tool for Coding in Lossless Image Compression Radu Rădescu University POLITEHNICA of Bucharest, Faculty of Electronics, Telecommunications.
Streaming and Content Delivery SECTIONS 7.4 AND 7.5.
COMPUTER GRAPHICS. Can refer to the number of pixels in a bitmapped image Can refer to the number of pixels in a bitmapped image The amount of space it.
CS 325 Introduction to Computer Graphics 04 / 12 / 2010 Instructor: Michael Eckmann.
Data Representation. What is data? Data is information that has been translated into a form that is more convenient to process As information take different.
_______________________________________________________________________________________________________________ PHP Bible, 2 nd Edition1  Wiley and the.
Resolution The resolution of an image is determined by the number of individually addressable points that make up the image, whether it is the number.
Visible Spectrum.
Image File Formats By Dr. Rajeev Srivastava 1. Image File Formats Header and Image data. A typical image file format contains two fields namely Dr. Rajeev.
Graphics and Image Data Representations 1. Q1 How images are represented in a computer system? 2.
Storing Graphics Nat 5 Data Representation Lesson 4a: Storing Graphics
CSS Layouts CH 13.
Hierarchical Clustering: Time and Space requirements
Tracking the eyes using a webcam
Computer Science Higher
Digital Photo editing with Photoshop
Fractal image compression
105 Color Images on MSX1 © 2006 Daniel Vik -
GRAPHICS Source:
Images in Binary.
What do these words mean to you?
Chapter 2 Data Representation.
How Computers Store Data
Presentation transcript:

_______________________________________________________________________________________________ © 2006 Daniel Vik Color Images on MSX1 © 2006 Daniel Vik -

_______________________________________________________________________________________________ © 2006 Daniel Vik - Screen 2 Images Max 2 colors per 8x1 block Background color Foreground color A screen 2 image is made up of 8x1 pixel sized blocks, which each has a background and a forground color from the palette: The MSX1 palette consists of 15 unique colors:

_______________________________________________________________________________________________ © 2006 Daniel Vik - Interlacing += Switching between two different screen 2 images every frame (50 times/second) creates an illusion of more colors than available in each image alone. For example:

_______________________________________________________________________________________________ © 2006 Daniel Vik - Interlaced palette A screen 2 image can show 15 different colors. By interlacing two screen 2 images its possible to “mix” colors to create a palette with up to 105 unique colors:

_______________________________________________________________________________________________ © 2006 Daniel Vik - Interlaced blocks Since the interlaced image is constructed from two screen 2 images, the available colors in a 8x1 block are the combination of the foreground and background colors of the two images: COLOR 1: background of image 1 + background of image 2 COLOR 2: background of image 1 + foreground of image 2 COLOR 3: foreground of image 1 + background of image 2 COLOR 4: foreground of image 1 + foreground of image 2 So not only do we get more colors, we also can use four different colors instead of two in each 8x1 block.

_______________________________________________________________________________________________ © 2006 Daniel Vik - Interlaced blocks example Even FrameOdd FrameMix

_______________________________________________________________________________________________ © 2006 Daniel Vik - Encoding algorithm Objective:Find the two 8x1 screen 2 blocks that combined give the colors closest to the source image. Converting a 24-bit RGB bitmap into interlaced screen 2 format is done by first dividing the RGB image into 8x1 pixel sized blocks.

_______________________________________________________________________________________________ © 2006 Daniel Vik - Cost Function To find a color x in the 105 color palette that best matches a color y in the source image a cost function is used: Q(x, y) = ( x r - y r ) 2 + ( x g - y g ) 2 + ( x b - y b ) 2 The cost in is the Minimum Square Error calculated in the RGB space but other cost functions can also be used. Doing cost calculations in the YUV space sometimes give better results, especially the intensity levels (gray tones). But it may give quite big color errors because the MSX palette has so few colors. A good way of getting both good color and intensity accuracy is to use YRGB in the cost function.

_______________________________________________________________________________________________ © 2006 Daniel Vik - Color Table An interlaced 8x1 block can hold up to four different colors but not all combinations from the 105 color palette can be used in one block. There are only 6020 ways the foreground and background colors can be combined in one block. The encoding algorithm uses a table T k,i with the 6020 different color combinations for the 8x1 block. Each entry T k in the table contains four RGB values that are made up of foreground and background colors of an even and an odd image.

_______________________________________________________________________________________________ © 2006 Daniel Vik - Cost calculation Next the cost q k of using a color combination T k to match the 8x1 block from the source image B n is calculated. The cost for one pixel n in the block is the color T k,i that gives the lowest cost value when compared with the pixel B n in the source image. The cost q k for the entire block is the sum of the cost of each individual pixel: q k = ∑ n=1..8 min( Q(T k,1, B n ), Q(T k,2, B n ), Q(T k,3, B n ), Q(T k,4, B n ) ) When the cost for all color combinations are calculated, the lowest value d k tells which color combination T k best matches the source image.

_______________________________________________________________________________________________ © 2006 Daniel Vik - Final Steps Once the lowest cost color combination T best is found, the foreground and background colors for the even and odd screen 2 image that made up the RGB values in T best are saved. The patterns of the two images are calculated indirectly in the cost calculation, but given the index i of the color with the lowest cost T best,i it is possible to tell whether a foreground or background color should be used for a pixel in the two screen 2 images.