Inputs and Outputs. Review. Outline. May 4, Image and video coding: A big picture

Similar documents
Image and video encoding: A big picture. Predictive. Predictive Coding. Post- Processing (Post-filtering) Lossy. Pre-

Lecture 2 Video Formation and Representation

So far. Chapter 4 Color spaces Chapter 3 image representations. Bitmap grayscale. 1/21/09 CSE 40373/60373: Multimedia Systems

Contents. xv xxi xxiii xxiv. 1 Introduction 1 References 4

Ch. 1: Audio/Image/Video Fundamentals Multimedia Systems. School of Electrical Engineering and Computer Science Oregon State University

Computer and Machine Vision

Lecture 2 Video Formation and Representation

Advanced Computer Networks

06 Video. Multimedia Systems. Video Standards, Compression, Post Production

Module 1: Digital Video Signal Processing Lecture 5: Color coordinates and chromonance subsampling. The Lecture Contains:

Digital Media. Daniel Fuller ITEC 2110

Lecture 2 Video Formation and Representation

Lecture 1: Introduction & Image and Video Coding Techniques (I)

Multimedia. Course Code (Fall 2017) Fundamental Concepts in Video

Video 1 Video October 16, 2001

Understanding IP Video for

10 Digital TV Introduction Subsampling

Research Topic. Error Concealment Techniques in H.264/AVC for Wireless Video Transmission in Mobile Networks

RECOMMENDATION ITU-R BT Methodology for the subjective assessment of video quality in multimedia applications

Motion Video Compression

Audio and Video II. Video signal +Color systems Motion estimation Video compression standards +H.261 +MPEG-1, MPEG-2, MPEG-4, MPEG- 7, and MPEG-21

ELEC 691X/498X Broadcast Signal Transmission Fall 2015

Chapter 2 Video Coding Standards and Video Formats

5.1 Types of Video Signals. Chapter 5 Fundamental Concepts in Video. Component video

Module 8 VIDEO CODING STANDARDS. Version 2 ECE IIT, Kharagpur

Chapter 3 Fundamental Concepts in Video. 3.1 Types of Video Signals 3.2 Analog Video 3.3 Digital Video

To discuss. Types of video signals Analog Video Digital Video. Multimedia Computing (CSIT 410) 2

PAL uncompressed. 768x576 pixels per frame. 31 MB per second 1.85 GB per minute. x 3 bytes per pixel (24 bit colour) x 25 frames per second

A Novel Approach towards Video Compression for Mobile Internet using Transform Domain Technique

Man-Machine-Interface (Video) Nataliya Nadtoka coach: Jens Bialkowski

1. Broadcast television

ARTEFACTS. Dr Amal Punchihewa Distinguished Lecturer of IEEE Broadcast Technology Society

An Overview of Video Coding Algorithms

Chrominance Subsampling in Digital Images

Intra-frame JPEG-2000 vs. Inter-frame Compression Comparison: The benefits and trade-offs for very high quality, high resolution sequences

Digital Image Processing

MULTIMEDIA TECHNOLOGIES

COMP 249 Advanced Distributed Systems Multimedia Networking. Video Compression Standards

Video Compression. Representations. Multimedia Systems and Applications. Analog Video Representations. Digitizing. Digital Video Block Structure

Understanding IP Video for

Television History. Date / Place E. Nemer - 1

CHAPTER INTRODUCTION:

Understanding PQR, DMOS, and PSNR Measurements

Information Transmission Chapter 3, image and video

EECS150 - Digital Design Lecture 12 Project Description, Part 2

Video (Fundamentals, Compression Techniques & Standards) Hamid R. Rabiee Mostafa Salehi, Fatemeh Dabiran, Hoda Ayatollahi Spring 2011

Understanding Human Color Vision

Research Article. ISSN (Print) *Corresponding author Shireen Fathima

Video Compression Basics. Nimrod Peleg Update: Dec. 2003

!"#"$%& Some slides taken shamelessly from Prof. Yao Wang s lecture slides

AN MPEG-4 BASED HIGH DEFINITION VTR

Transitioning from NTSC (analog) to HD Digital Video

SERIES J: CABLE NETWORKS AND TRANSMISSION OF TELEVISION, SOUND PROGRAMME AND OTHER MULTIMEDIA SIGNALS Measurement of the quality of service

Margaret H. Pinson

Picture Quality Analysis Software

Picture Quality Analysis Software

Luma Adjustment for High Dynamic Range Video

Minimizing the Perception of Chromatic Noise in Digital Images

Video Basics. Video Resolution

UC San Diego UC San Diego Previously Published Works

OVE EDFORS ELECTRICAL AND INFORMATION TECHNOLOGY

Getting Images of the World

Picture Quality Analysis Software PQASW Datasheet

Chapter 1 INTRODUCTION

Multimedia Communications. Video compression

Video Demystified. A Handbook for the Digital Engineer. Fifth Edition. by Keith Jack

Video Codec Requirements and Evaluation Methodology

Error concealment techniques in H.264 video transmission over wireless networks

CMPT 365 Multimedia Systems. Mid-Term Review

Glossary Unit 1: Introduction to Video

Color Image Compression Using Colorization Based On Coding Technique

EFFICIENT HEVC LOSS LESS CODING USING SAMPLE BASED ANGULAR INTRA PREDICTION (SAP) PAVAN GAJJALA. Presented to the Faculty of the Graduate School of

ELEG5502 Video Coding Technology

Analog and Digital Video Basics

How Does H.264 Work? SALIENT SYSTEMS WHITE PAPER. Understanding video compression with a focus on H.264

Welcome Back to Fundamentals of Multimedia (MR412) Fall, ZHU Yongxin, Winson

Content storage architectures

Visual Communication at Limited Colour Display Capability

ATSC Candidate Standard: A/341 Amendment SL-HDR1

Part II Video. General Concepts MPEG1 encoding MPEG2 encoding MPEG4 encoding

Rounding Considerations SDTV-HDTV YCbCr Transforms 4:4:4 to 4:2:2 YCbCr Conversion

Picture Quality Analysis Software PQASW Datasheet

Implementation of an MPEG Codec on the Tilera TM 64 Processor

Multimedia Communications. Image and Video compression

QUALITY ASSESSMENT OF VIDEO STREAMING IN THE BROADBAND ERA. Jan Janssen, Toon Coppens and Danny De Vleeschauwer

Analog and Digital Video Basics. Nimrod Peleg Update: May. 2006

MPEG-2. ISO/IEC (or ITU-T H.262)

Lecture 23: Digital Video. The Digital World of Multimedia Guest lecture: Jayson Bowen

A New Standardized Method for Objectively Measuring Video Quality

Video compression principles. Color Space Conversion. Sub-sampling of Chrominance Information. Video: moving pictures and the terms frame and

Steganographic Technique for Hiding Secret Audio in an Image

H.261: A Standard for VideoConferencing Applications. Nimrod Peleg Update: Nov. 2003

Picture Quality Analysis System

CUFPOS402A. Information Technology for Production. Week Two:

Digital Television Fundamentals

ATSC vs NTSC Spectrum. ATSC 8VSB Data Framing

EMBEDDED ZEROTREE WAVELET CODING WITH JOINT HUFFMAN AND ARITHMETIC CODING

Multimedia Systems. Part 13. Mahdi Vasighi

H.264/AVC analysis of quality in wireless channel

Midterm Review. Yao Wang Polytechnic University, Brooklyn, NY11201

Transcription:

Lecture/Lab Session 2 Inputs and Outputs May 4, 2009 Outline Review Inputs of Encoders: Formats Outputs of Decoders: Perceptual Quality Issue MATLAB Exercises Reading and showing images and video sequences Getting familiar with color spaces Examining spatial and temporal redundancies Examining psychovisual redundancies Measuring perceptual quality with PSNR 1 Image and video coding: A big picture Predictive Review Input Pre- Processing Lossy Lossless Post- Processing Visual Quality Measurement Predictive Encoded 110 11001 Decoded Post- Processing Lossy Lossless Pre-Processing 3 1

Some terms HSV/HSL (HSI/HSB) color spaces Luminance = Brightness = Lightness = Intensity = Value Hue: Saturation = Colorfulness = Chroma = Vividness = Purity Chromaticity = Hue + Saturation => It is a 2-tuple. Chromaticity Diagram: a 2-D diagram showing chromaticities Color = Luminance + Chromaticity => So, it is a 3-tuple. Chrominance (Chroma) = Color Difference Color Space: a 3-D space of colors Color Mixing Systems (light) vs. Color Appearance Systems (perception) Gamut: range of colors (in a color space) JND: Just-Noticeable Difference (50% accuracy) HSV color space HSL color space 4 5 srgb vs. CIE 1931 XYZ color spaces Inputs of Encoders: Formats 6 2

Color conversion in image/video encoding Color conversion: R G B => Y C b C r Input Pre-Processing Color Space RGB R G B Conversion Y UV/ Y P b P r 001110101001 Encoded A/D Conversion Y C b C r Chroma Subsampling Other Parts of Encoding Process 8 R,G,B [0,1],,, [, k r, k b (0,1), (, k r+k b<1 Y =k r *R +(1-k r -k b )*G +k b *B [0,1] P b =0.5*(B -Y )/(1-k b ) [-0.5,0.5] P r =0.5*(R -Y )/(1-k r ) [-0.5,0.5] Values of k b and k r can be different! ITU-T BT.601 (SDTV): k r =0.229, k b =0.114 ITU-T T BT.709 (HDTV): k r =0.2126, k b =0.0722 ANSI/SMPTE 240M-1995 (HDTV): k r =0.212, k b =0.087 Y P b P r => Y C b C r (taking MPEG-2 as an example) Y =219*Y +16 [16,235] C b =224*P b +128 [16,240] C r =224*P r +128 [16,240] 9 Chroma subsampling formats A 4X2 Image P(1,1) P(1,2) P(1,3) P(1,4) P(2,1) P(2,2) P(2,3) P(2,4) Y C b C r Y Sampling Locations C b Sampling Locations C r Sampling Locations 4:4:4 4 4 4 ALL ALL ALL 4:2:2 4 2 2 ALL P(1,1) P(1,3) P(2,1) P(2,3) P(1,1) P(1,3) P(2,1) P(2,3) 4:2:0 4 1 1 ALL P(1,1) P(1,3) P(1,1) P(1,3) 4:1:1 4 1 1 ALL P(1,1) P(2,1) P(1,1) P(2,1) Progressive vs. Interlacing Progressive mode: each row of a frame is scanned one by one Interlacing mode: a frame is divided into two fields odd rows are scanned first and even rows later. Benefit: bandwidth saving close to ½ 1920 1080 60H interlacing HDTV: 1920/2 1080 60 24= 1492992000 1.4 G bits/second 1280 720 60H progressive HDTV: 1280 720 60 24= 1327104000 1.236 G bits/second Problem: 10 11 3

Interlacing mode: Frames vs. Fields Interlacing mode: Frames vs. Fields A frame may be divided into two fields Top filed + Bottom field A frame may be divided into two fields Top filed + Bottom field 12 Interlacing mode: Frames vs. Fields 13 Digital image formats A frame may be divided into two fields Top filed + Bottom field No standard spatial resolutions Uncompressed images 14 For research purpose: 2n 2n, such as 128 128, 256 256, 512 512, 1024 1024 BMP (*.bmp): fileheader + infoheader + [palette] + data (row by row, bottom-up) Netpbm formats: PBM/PGM/PPM (*.pbm/*.pgm/*.ppm) Losslessly compressed images TIFF = Tagged Image File Format (*.tiff/*.tif) PNG = Portable Network Graphics (*.png) GIF = Graphics Interchange Format (*.gif) up to 256 colors Lossily compressed images JPEG (*.jpg), JPEG2000 (*.jp2/*.j2k) 15 4

Digital video formats CIF = Common Intermediate Format (since H.261) CIF (Full CIF = FCIF) = 352 288 QCIF (Quarter CIF) = 176 144 SQCIF (Sub Quarter CIF) = 128 96 4CIF = 4 CIF = 704 576 16CIF = 4 4CIF = 1408 1152 SIF = Source Input Format (since MPEG-1) 625/50 (TV: PAL/SECAM) = 352 288/360 288 525/59.94 (TV: NTSC) = 352 240/ 360 240 Sub-SIF (Computers) = 320 288 or 384 288 YUV video file format (.yuv/.cif/.qcif/.sif/ ) Planar formats YUV = YV12 = I420 = IYUV (4:2:0) YV16 (4:2:2) Packed formats UYVY = UYNV = Y422 (4:2:2) YUY2 = YUNV = V422 = YUYV (4:2:2) More info available at http://www.fourcc.org/yuv.php 16 17 YUV4MPEG2 format (.y4m) File Header File signature: YUV4MPEG2 Parameters Width, height and frame rate: Wxxx Hyyy Fa:b Interlacing: Ip (progressive), It (top field first), Ib (bottom field first), Im (mixed mode, detailed in frame headers) Aspect ratio: Aa:b Color space (Chroma format): C4xx... Frames Comment: X. Frame Header FRAME + a number of parameters (optional) + 0x0A Frame (YUV planar format) More information is available at http://wiki.multimedia.cx/index.php?title=yuv4mpeg2 Multimedia container/wrapper formats AVI = Audio Video Interleave (*.avi) FLV = Flash Video (*.flv) ASF = Advanced Systems Format (*.asf) MPEG-TS (Transport Stream) & MPEG-PS (Program Stream) (*.mpg/*.ts/*.ps) MP4 = MPEG-4 Part 14 (*.mp4) => 3GP (*.3gp/*.3g2) MOV (Quicktime) (*.mov) RealMedia (*.rm/*.rmvb) 18 19 5

Image and video coding: Quality issue Quality Mt Meterics Outputs of Decoders: Perceptual Quality Issue Input image/video Visual Quality Measurement Decoded image/video Encoder Decoder Encoded image/video 21 Visual quality measurement: Subjective DSIS (Double Stimulus Impairment Scale) DSCQS (Double Stimulus Continuous Quality Scale) SSCQE (Single Stimulus Continuous Quality Evaluation) Some measurement methods have been standardied: ITU-R BT.500, ITU-R BT.710, ITU-T P.910 Visual quality measurement: Objective Two images: the original one f(x,y) and the decoded one f (x,y) MSE = Mean Squared Error 1 MN X (f 0 (x, y) f(x, y)) 2 x,y SNR = Signal-to-Noise Ratio à P! x,y (f 0 (x, y)) 2 10 log 10 P x,y (f 0 (x, y) f(x, y)) 2 PSNR = Peak Signal-to-Noise Ratio Ã! L 2 10 log 10 P x,y (f 0 (x, y) f(x, y)) 2 22 23 6

Visual quality measurement: Objective Visual quality measurement: Objective SSIM = Structural Similarity Index SSIM = The original image PSNR=32.7 db PSNR=37.5 db (2E(f (x, y)f 0(x, y)) + C1 ) (2σf,f 0 + C2 ) ³ (E(f(x, y))2 + E(f 0 (x, y))2 + C1 ) σf2 + σf2 0 + C2 VQM = Video Quality Metric MPQM = Moving Pictures Quality Metric NQM Q = Noise Q Quality y Measure Research Question: PSNR is not good enough to perfectly reflect visual quality, more advanced metrics considering HVS are wanted. Some objective metrics are being standardied by the ITU-T. 24 25 Reading and Showing MATLAB Exercises Read an image. g Show an image. f=imread( Images/lena_color.bmp ); imshow(f); Read a frame from a YUV video. Show a video frame frame. f=yuvread( Video/news.qcif,1); imshow(ycbcr2rgb(yuv4xx_444(f))); Play back a YUV video. yuvplay( Video/news.qcif, All ); 27 7

Getting familiar with color spaces Show YUV planes of a video. f=yuvread( test.cif',1); subplot(2,2,1:2); imshow(f.y); subplot(2,2,3); imshow(f.u); subplot(2,2,4); imshow(f.v); Try to add pseudo-colors to U- and V-planes. Tip: assume Y=0.5 and another chroma channel is 0. Try to read a RGB image and convert it to YUV color space. YUV video player => Y4M video player Read the code of YUV video player. Two files: YUVplayer.m and YUVplayer.fig. Run guide YUVplayer.fig to open the second file. Read MATLAB help documents to learn how to design GUI with MATLAB. Try to implement a Y4M video player. This can be a take-home assignment. 28 29 8