Computer and Machine Vision
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1 Computer and Machine Vision Deeper Dive into MPEG Digital Video Encoding January 22, 2014 Sam Siewert
2 Reminders CV and MV Use UNCOMPRESSED FRAMES Remote Cameras (E.g. Security) May Need to Transport Frames Capture Over Network to CV/MV Processor We NEED to Understand Both! BEWARE of LOSSY COMPRESSION I-Frame ONLY or MJPEG Decent Compromise of Both Sam Siewert 2
3 MPEG: Order Of Operators #1 #2C #2B #2A #3 #1: POINT (Pixel) Encoding #2 A-C: Macro-Block Lossy Intra-Frame Compression #3: Motion-Based Compression in Group of Pictures Sam Siewert 3
4 Step #1 RGB to YCrCb 4:4:4 24-bit (Lossless) For every Y sample in a scan-line, there is also one CrCb sample Each Y (Y7:Y0), Cr (Cr7:Cr0), & Cb (Cb7:Cb0) Sample is 8 bits No compression between RGB and YCrCb 4:4:4 (both 24 bits/pixel) Typically a Post Production, CEDIA or DCI format ,480 76,799 = Y, Cr, and Cb sample = Y sample only Sam Siewert 4
5 Step #1 RGB to YCrCb 4:2:2 (Lossy) For every 2 Y samples in a scan-line, one CrCb sample Each Y (Y7:Y0), Cr (Cr7:Cr0), & Cb (Cb7:Cb0) Sample is 8 bits Two RGB Pixels = 48 bits, Whereas Two YCrCb is 32 bits, or 16 bits per pixel vs. 24 bits per pixel (33% smaller frame size) bit to 32 bit 76,480 76,799 = Y, Cr, and Cb sample = Y sample only Sam Siewert 5
6 Step #1 RGB to YCrCb 4:2:0 (Lossy) For every 4 Y samples in a scan-line, one CrCb sample Each Y (Y7:Y0), Cr (Cr7:Cr0), & Cb (Cb7:Cb0) Sample is 8 bits Two RGB Pixes = 48 bits, Whereas Four YCrCb is 48 bits, or 12 bits per pixel on average vs. 24 bits per pixel (50% smaller) ,480 76,799 = Cr, Cb sample = Y sample only Sam Siewert 6
7 Step #2 Convert to 8x8 Macroblocks and Transform Aspect Ratios Designed to Fit 8x8 Macroblock E.g. 640 x 480 => 80 x 60 Macroblocks Discrete Cosine Transform Applied to Each 8x8 Spatial Intensity to Frequency Transform Applied on X Axis (Row) Applied on Y Axis (Column) Set up for Intra-frame (I-frame) Compression Sam Siewert 7
8 Convolution Concepts Math operation on 2 functions, that produces a 3 rd Point Spread Function Sharpen meets this Definition So do Many Mask Operations applied to Pixel Neighborhoods 2 impulses, f(t), g(x t) Area inside intersection f convolved with g over t Sam Siewert 8
9 DCT Discrete Cosine Transform Convolution of Image with Discrete Cosine See De-convolved to restore image from Convolved Image DCT Inverse DCT Sam Siewert 9
10 DCT Concepts F(x) is a sum of sinusoids (with frequency, amplitude) DCT operates of a discrete number of samples Can derive DC sum at any x, even where F(x) not known N x N Macro-block has Zero Frequency DC at 0,0 Increasing Horizontal Frequency Increasing Vertical Frequency Can De-convolve (inverse DCT, or idct) Can Eliminate High Frequency Horizontal and Vertical Terms Minimal Losses from Truncation (otherwise lossless) Loss of High Frequency Image Features (What are These?) Sam Siewert 10
11 Basic Concept of Waveforms Complex Waveform is Sum of Simple Fundamentals Simple Fundamentals Can Be Derived from Complex Sam Siewert 11
12 Scanline DCT Example Small Losses Due to DCT, idct Numerical Truncation Larger Losses Due to H.O.T. Quantization and Truncation Fundamentals.xlsx Sam Siewert 12
13 What Is Lost with DCT Quantization? Noise More Than Anything Else Complex XY Variable Patterns (Real Science Data?) Complex Tiling Higher Frequency X Higher Frequency Y Terms Can Still be Ignored Complex Wood Texture Most Detail in X Far Less in Y Randomized Texture Image High X Detail High Y Detail Most Loss of Detail, But Noisy Sam Siewert 13
14 Step #2A: Macro-block Discrete Cosine Transform 8x8 Pixel Block Macro-block SD NTSC 720x480 (90x60 Macro-blocks), 3:2 Aspect Ratio HD x720 (160x90 Macro-blocks), 16:9 AR HD x1080 (240x135 Macro-blocks), 16:9 AR Sam Siewert 14
15 Step #2B: Macro-block Quantization (Lossy) Apply Weighting and Scaling 8x8 to DCT Produces Lots of Repeated Values (and Zeros) Compared to Original Sam Siewert 15
16 Decode Process for #2A-B Sam Siewert 16
17 How Lossy is the Decode Macro- Block? Sam Siewert 17
18 OpenCV Macroblock DCT Example Same Cactus 320x240 with 80x80 DCT Macroblocks DCT idct Same Cactus 320x240 Again with 8x8 DCT Macroblocks DCT idct Sam Siewert 18
19 Mathematics for 2D DCT Frequency Variation on X and Y axes from top left to bottom right Straight-forward Algorithm Based on 2D Equation is O(n 2 ) per dimension Like Cooley-Tukey for DFT, a DCT Algorithm that is O(n*log 2 (n)) has been formulated (Arai, Y.; Agui, T.; Nakajima, M. - Numerical Recipes: The Art of Scientific Computing (3rd ed.)) mis_code/dct2/dct2.c Sam Siewert 19
20 Step #2C: Macro-block Run-Length and Huffman Encoding Zig-Zag Run-Length Encoding to Exploit Repeated Data and Zeros found in H.O.T. of Quantized DCT 86, 1, 7, -5, -1, 0, 1, 0, 0, 2, -1, 1, 0, -1, 0, 0, 0, 0, -1, 0, 0, Becomes: Sam Siewert 20
21 Huffman Applied to RLE Data Huffman Tables for MPEG-2 Macro-Blocks Defined in (Lossless) Compression Based on Probability of Occurance Shannon s Source Coding Theory: log 2 (P), P=probability of occurrence, Binary encoding of Symbols Sam Siewert 21
22 Step #3: Group of Pictures Concept Transmit Change-Only Data I-Frame Compressed Only Intra-Frame By Methods #2A-2C to Macro-Blocks I-Frame Can Be Decoded Alone P-Frame is Differences Only Over the GoP B-Frame is Differences Only Between Both I-Frame and Closest P-Frame Difference Data Can be Further Encoded with Lossless Methods Without Steps 2A-C, Specifically Quantization, and With High Motion Video, Could Blow-Up Sam Siewert 22
23 Group of Pictures: High Level View Sam Siewert 23
24 Overall MPEG YCrCb Compression Performance Standard Definition 720x480x2 30fps Requires 20MB/sec (200 Mbps) Uncompressed Typical 3.75 Mbps, > 50x Compression Typical 1.5 Mbps, > 100x Compression 10 to 20 Programs on QAM 256 (48Mbps, 6MhZ/Ch) 10 MPEG-4 Programs on ATSC 8VSB (19.39 Mbps, 6MhZ/Ch) HD 720p 30fps Requires 53MB/sec (530Mbps) Uncompressed Typical 20 Mbps, > 25x Compression Typical 10 Mbps, > 50x Compression HD 1080p (1920x1080x2, 30fps Requires 120MB/sec (1200Mbps) Uncompressed Typical MPEG-2, 45 Mbps, > 30x Compression Typical 20 Mbps, > 60x Compression Sam Siewert 24
25 Parsing an Elementary Video Stream Many 188-Byte Packet Types and Header Allows for Multi-plexing of many Video and Audio Streams on a Carrier Sam Siewert 25
26 MPEG-4 vs. MPEG-2 MPEG-2 Defined by ISO , Leverages MPEG-1 (Motion Picture Experts Group 1988) Widely Used for Digital Video Digital Cable TV, DVD Transport Stream designed for Broadcast (Lossy, No Beginning or End of Stream) ATSC Advanced Television Systems Committee (HDTV Broadcast) 8VSB Modulation 8 level Vestigal Sideband Modulation, 6MhZ channel, Mbps, Reed-Solomon Error Correction Up to 1080p (1920x1080) Video Resolution AC-3 (Dolby) Audio DVB Digital Video Broadcast (Europe, Satellite) Program Stream designed for Playback Media (DVD, Flash, HDD, etc.) MPEG-4 Defined by ISO (1998) Leverages MPEG-2 Standards for Program/Transport, Encode/Decode Better Compression Rates (improved motion prediction for P,B frames), MPEG-4 Part-10 (H.264), e.g. Blu-Ray Extensions for Digital Rights Management Advanced Audio Encoding Becoming More Widely Deployed for HD and Because of Lower Bit-Rate Transport Streams Sam Siewert 26
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