Digital Media. Lecture 10: Video & Compression. Georgia Gwinnett College School of Science and Technology Modified from those of Dr.

Transcription

1 Digital Media Lecture 10: Video & Compression Georgia Gwinnett College School of Science and Technology Modified from those of Dr. Jim Rowan

2 Coping with Video Size Consider human vision limitations 1) Chrominance sub-sampling Compression - two versions 2) Spatial 3) Temporal differencing vectoring

3 Coping with Video Size Consider human vision limitations 1) Chrominance sub-sampling Compression - two versions 3) spatial 4) Temporal differencing vectoring

4 Chrominance sub-sampling Humans can t distinguish changes in color as well as they can distinguish luminance changes pling In our cameras Of every 4 frames store the luminance for each frame only store a proportion of the color info 4:2:0

5 Chrominance sub-sampling

6 Chrominance sub-sampling Luminance, Cr, Cb

7 Coping with Video Size Aside from screen size and frame rate... Consider human vision limitations 1) Chrominance sub-sampling Compression - two versions 2) spatial 3) Temporal differencing vectoring

8 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 Our video cameras do this, compressing each frame to jpeg

9 Coping with Video Size Aside from screen size and frame rate... Consider human vision limitations 1) Chrominance sub-sampling Compression - two versions 2) spatial 3) Temporal differencing vectoring

10 Temporal Compression differencing Use the Difference in two frames A naive approach can result in good compression Works well for a small amount of movement Security cameras spend most of their time seeing the same thing all night long A Tarantino film? not so much Most pixels change with nearly every frame

11 Image Differencing To subtract one image from the next Do it one pixel at a time red minus red green minus green blue minus blue Store the difference To play it back Play frame one 1 Add frame 2 to frame 1 Next, an example in black & white

12 Temporal Compression differencing

13 Example 1, the difference of two identical images

14 Example 1, the difference of two identical images The result ===>

15 Example 2, the difference of two similar images

16 Example 2, the difference of tw similar images The result ===>

17 Temporal Compression vectoring When an OBJECT moves compute its trajectory fill in the resulting exposed background Captured Frame 1 Captured Frame 2 Stored Background Stored Object BUT there s a problem... why isn t this an easy thing to do? Movement Vector

18 More on differencing 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...

19 MPEG terminology MPEG-2 iframe (interframe prediction) pframe (forward predicted) bframe (backward predicted) GOP (group of pictures) ntra_coded_frames_.28or_slices_or_i-fra mes_or_key_frames.29

20 MPEG terminology iframe: -a keyframe -spatially compressed (a fully specified image) pframe -predicted frame -contains only the difference between the current frame and the previous iframe (smaller in size than iframe) bframe -bi-predicted frame -contains difference between current and both the preceding and following iframes -even smaller in size than iframe

21 MPEG terminology Group(s) Of Pictures (GOPs) All spatially (intra-frame) compressed The largest Smaller but more computation Spatially compressed and predictive (difference) Spatially compressed forward and backward predictive (difference) Play sequence: Transmit sequence: Smallest but more computation and it is transported out of order!