Dr. Jim Rowan ITEC 2110 Video Part 2 Digital Media Dr. Jim Rowan ITEC 2110 Video Part 2

Digital Video Standards Even though digital video COULD be much less complicated... it isn’t because... Backward compatibility requirement new equipment must create signals that can be handled by older equipment Originally... TV signals (analog) needed to be converted to digital format

Digital Video Standards... Broadcast TV (analog) PAL 576 (625) lines, 50 fields, 25 frames NTSC 480 (576) ~60 fields, ~30 frames Non-broadcast TV PAL is 720x576 NTSC is 720x480

Digital Video Standards... Digital from NTSC and PAL are ANALOG standards Each define a number of lines on the screen (the Y dimension) But what about the other dimension, the X?... PAL is 720 NTSC is 720 Each line is a continuous (analog) signal which has to be converted to digital... How do you do that? SAMPLE the analog data! But directly sampling for each pixel results in a data stream of 20 Mbytes/ second... HUGE!

Coping with Video Size Consider human vision limitations 1) Use algebra to compute part of the signal 2) Chrominance sub-sampling Compression - two versions 3) spatial 4) temporal

Coping with Video Size Consider human vision limitations 1) Use algebra to compute part of the signal 2) Chrominance sub-sampling Compression - two versions 3) spatial 4) temporal

1) Use algebra to compute part of the signal To reduce the data stream you can consider human vision again Human eyes are less sensitive to color changes than luminance Decision: Take fewer samples for color than luminance Without sub-sampling... for each pixel on the screen 4 things will have to be encoded luminance, red, blue, green

1) Use algebra to compute part of the signal Designers realized that Green contributes the most to intensity, Red is next and Blue hardly contributes anything to luminance Based on this, it was decided to use a formula for luminance Y = 0.2125R+0.7154G+0.0721B With this we only have 3 (not 4) data elements to transmit The 4th element (green luminance) can be calculated - this results in a 25% data reduction -Y (luminance) -Cb (blue chrominance) -Cr (red chrominance)

Calculating the 4th color component Known as the Y’CbCr model Y = 0.2125R+0.7154G+0.0721B Solve for Cg (green): Y - 0.0721B - 0.2125R = 0.7154G 0.7154G = Y - 0.0721B - 0.2125R G = (Y - 0.0721B - 0.2125R) / 0.7145 There is a use for algebra!

Coping with Video Size Consider human vision limitations 1) Use algebra to compute part of the signal 2) Chrominance sub-sampling Compression - two versions 3) spatial 4) temporal

Chrominance sub-sampling Humans can’t distinguish changes in color as well as they can distinguish luminance changes http://en.wikipedia.org/wiki/Chroma_subsampling Of every 4 frames store the luminance only store a proportion of the color info

Chrominance sub-sampling L:R:B LRGB LRGB LRGB LRGB 4:4:4 LRB L LRB L 4:2:2 CCIR 601 video sampling LRB L L L 4:1:1 NTSC DV LR L L L 4:2:0 PAL DV notice the inconsistency? L L L LB

NTSC & PAL weirdness (sidebar) Define different screen sizes Define different frame rates Both have the same aspect ratio of 4:3 BUT... they each are digitized (through sampling) to the same screen size The result? The pixels are not square PAL is taller than it is wide NTSC is wider than it is tall

Coping with Video Size Aside from screen size and frame rate... Consider human vision limitations Use algebra to compute part of the signal Chrominance sub-sampling Compression - two versions spatial temporal

Coping with Video Size Spatial compression Individual images can be compressed using the techniques discussed in the bitmapped section Doesn’t result in very much compression for video Doesn’t take into consideration the other frames that come before or after it

Coping with Video Size Aside from screen size and frame rate... Consider human vision limitations Use algebra to compute part of the signal Chrominance sub-sampling Compression - two versions spatial temporal

Temporal Compression 1 Use the Difference in two frames naive approach can result in good compression works well for a small amount of movement A Tarantino film? not so much... MPEG terminology: I-picture (intra-frame) the first frame P-picture (predictive) all subsequent frames

Temporal Compression 2 When an OBJECT moves compute its trajectory fill in the resulting exposed background vector BUT there’s a problem... why isn’t this an easy thing to do?

continue next class (TuTh) (W) differencing then vectors differencing AND vectors For example===>

In a very simplistic example… If we could store the bits that make up the background the bits that make up the fish Then we could make up the second frame by transmitting the distance the fish moved (a vector) the bits that need to be added back where the fish was

Forward and Backward The differencing can happen in a forward manner and a backward manner It might be more economical (in data size) to create a frame from a frame that follows it...

Pgs 217-219

BUT Temporal Compression 2 Bitmapped images do not have defined objects... that’s Vector graphics... What to do?

Temporal Compression 2 Define blocks of 16 x 16 pixels called a macroblock Compute all possible movements of the block within a short range Compute a vector to define that movement Store the movement vectors Compress the vectors

More on Temporal Compression Need some place to start from Can be forward or backward prediction Called KeyFrames pick a keyframe compute next image from that What happens when the scene completely changes? Pick a new key frame... But HOW? Requires powerful AI

Video Compression What does this? Coder/Decoder - Codec http://en.wikipedia.org/wiki/Video_codec encodes and decodes video can be symmetric it takes as long to compress as decompress can be asymmetric it takes longer to compress or decompress than it does to decompress to compress

A final worry... We have been talking about making video smaller There are a variety of techniques to do this Which to choose? It is a tradeoff between compression technique and its computational complexity

MPEG-4 Designed for streams that contain video, still images, animation, textures 3-D models Contains methods to divide scenes into arbitrarily shaped video objects The idea is that each object has an optimal compression technique BUT...

MPEG-4 Dividing a scene into arbitrarily shaped video objects is non-trivial so they drop back to the rectangular object position Quicktime and DivX use the rectangular video object idea Forward inter-frame compression Backward inter-frame compression Using the simpler technique reduces the computational complexity allowing it to be implemented on small devices like portable video players

Other codecs Cinepak, Intel Indeo & Sorenson All use “vector” quantization divides frame into rectangular blocks these frames are called “vectors” but they don’t represent movement or direction

Vectors as patches... Codec uses a collection of these “vectors” contains typical patterns seen in the frames textures, patterns, sharp and soft edges compares the “vectors” to the ones in the code book if it is close, it uses the code book entry (does this explain the patchwork painting of the screen when the digital signal goes bad?)

“Vector” quantization a frame contains indices into the code book reconstructs the image from “vectors” in the code book makes decompression very straight forward and efficient this makes the implementation of a player very easy What about the compression?

“Vector” quantization this is an asymmetric codec compression takes ~150 times longer than decompression Cinepak and Intel Indeo use temporal compression and simple differencing Sorenson uses motion compensation similar to the MPEG-4 standard

So... How do codecs vary? compression and decompression complexity affects the artifacts that are created affects the time required to carry them out affects the volume of the data stream created affects the type and expense of the equipment used affects whether or not it can be implemented in hardware of software

Comparison Bear in mind that this comparison is not absolute and will vary from frame to frame but in general... MPEG-4 detail is good (at the sacrifice of speed) DV detail is good but the biggest Sorenson loss of detail (see pg 218-219) Cinepak loss of detail smallest file

A word about QuickTime All standards so far have defined the data stream... not the file format QT is the defacto standard design of a component-based architectural framework allows plugins (components) to be developed by others Every QT movie has a “time base” records playback speed and current position relative to a time coordinate system to allow them to be replayed at the right speed on any system It keeps the visual synched with the audio If the playback speed of the device is not fast enough, QT drops frames keeping audio synchronization

More about QuickTime Plugins make it flexible so that it can accommodate new file formats comes with a standard set of plugins (components) compressor components include MPEG-4, Sorenson and Cinepak movie controller interface provides uniformity transcoder components exist to convert one format to another format supports true streaming and progressive download

Questions?