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Digital Video Compression
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  1. Digital Video Compression

  2. Compressing video can be anart and a science — but most of us don’t want to get that deep. We want to create videos, and quickly compress them for clear images and smooth playback without hours of trial and error experimentation. If you know your video is internet-bound, there are a few things you can do when you are shooting to make it look better on a computer screen. Compressing video

  3. COMPRESSION: A BASIC IDEA • Compression is the technique in which the original data is converted to a format that requires fewer bits, so that the data can be stored or transmitted more efficiently . • . The shrinking of the original size of the data to a compressed form is known as the compression of data. • The inverse of the compression process is decompression. If it produces an exact replica of the original data then the compression is lossless. • Lossy compression, usually applied to image data, does not allow reproduction of an exact replica of the original image, but has a higher compression ratio. • compression ratio = compressed form / original size.

  4. need for compression • The high bit rates that result from the various types of digital video make their transmission through their intended channels very difficult. • Even entertainment video with modest frame rates and dimensions would require bandwidth and storage space far in excess of that available from CD-ROM.

  5. Need… • Finally, even if the storage and transportation problems of digital video were overcome, the processing power needed to manage such volumes of data would make the receiver hardware very expensive.

  6. COMPRESSION TECHNIQUES: • A device (software or hardware) that compresses data is often know as an encoderor coder, whereas a device that decompresses data is known as a decoder. • General purpose: compression techniques can be used for any kind of data. • Intraframe compression: techniques work on images. • Interframe compression: techniques work on image sequences rather than individual images.

  7. Run Length Encoding: Run Length Encoding is a compression technique that replaces consecutive occurrences of a symbol with the symbol followed by the number of times it is repeated. For example, the string 111110000003355 could be represented by 15063252.

  8. Relative Encoding:Relative encoding is a transmission technique that attempts to improve efficiency by transmitting the difference between each value and its predecessor, in place of the value itself. Thus the values 15106433003 would be transmitted as 1+4-4-1+6-2-1+0-3+0+3.

  9. Arithmetic Coding: Arithmetic coding is usually more efficient than the more popular Huffman technique. Although more efficient than Huffman coding, arithmetic coding is more complex.

  10. Why can Images be Compressed? • Image compression can be achieved primarily because image data are highly redundant. • The degree of redundancy determines how much compression can be achieved. • 􀁺 Four types of redundancy can be identified: 􀁺 Spatial Redundancy 􀁺 Correlation between adjacent data points 􀁺 Temporal Redundancy 􀁺 Correlation between different frames in an image 􀁺 Spectral Redundancy 􀁺 Correlation between different color planes or sensors 􀁺 Limitation of Low-level Human Vision System 􀁺 Psycho-visual Redundancy 􀁺 Limitation of high-level Human Vision System

  11. Approaches to Video Compression • Intraframe compression treats each frame ofan image sequence as a still image. 􀁺 Intraframe compression, when applied to image sequences, reduces only the spatial redundancies present in an image sequence. • Interframe compression employs temporal predictions and thus aims to reduce temporal as well as spatial redundancies, increasing the efficiency of data compression. 􀁺 Example: Temporal motion-compensated predictive compression.

  12. INTRAFRAME COMPRESSION: Intraframe compression is compression applied to still images, such as photographs and diagrams. Intraframe compression techniques can be applied to individual frames of a video sequence. . Sub-sampling of images usually takes place in one of two ways. In the first, the original image is copied but only a fraction of the pixels from the original are used. Alternatively, sub-sampling can be implemented by calculating the average pixel value for each group of several pixels, and then substituting this average in the appropriate place in the approximated image.

  13. In this example the pixels in every second row and every second column are ignored. To compensate for this, the size of the remaining pixels is doubled.

  14. COARSE QUANTIZATION: • It is similar to sub-sampling in that information is discarded, but the compression is accomplished by reducing the numbers of bits used to describe each pixel, rather than reducing the number of pixels. • Each pixel is reassigned an alternative value and the number of alternate values is less than that in the original image. Quantization where the number of ranges is small is known as coarse quantization.

  15. INTERFRAME COMPRESSION: Interframe compression is compression applied to a sequence of video frames, rather than a single image. In general, relatively little changes from one video frame to the next. Interframe compression exploits the similarities between successive frames, to reduce the volume of data required to describe the sequence. • Difference Coding • Block Based Difference Coding • Block Based Motion Compensation

  16. Video Compression • Main addition over image compression: 􀁺 Exploit the temporal redundancy • 􀁺 Predict current frame based on previously coded frames • 􀁺 Three types of coded frames: 􀁺 I-frame: Intra-coded frame, coded independently of all other frames 􀁺 P-frame: Predictively coded frame, coded based on previously coded frame 􀁺 B-frame: Bi-directionally predicted frame, coded based on both previous and future coded frames

  17. MPEG 1 Group of Pictures (GOP)Structure • Composed of I, P, and B frames • 􀁺 Periodic I-frames enable random access into the coded bit stream. • 􀁺 Parameters: (1) Spacing between I frames (2) number of B frames between I and P frames

  18. Example Use of I-,P-,B-frames:MPEG 1 Group of Pictures (GOP) • Arrows show prediction dependencies between frames

  19. Motion Prediction

  20. Motion Estimation

  21. MPEG: the Standard • Moving Picture Experts Group: A Video Compression Standardfor Multimedia Applications • Started in 1988 • Had to come up with a draft of the standard by 1990 • Received MPEG Proposal from mostly commercial companies • Standards under International Organization for standardization (ISO) and International Electro technical Commission (IEC) • Official name is: ISO/IEC JTC1 SC29 WG11

  22. MPEG vs. Competitor • Generally produces better quality than the other formats such as: • Video for Window • MPEG audio/video compression can be used in many applications: • DVD player • HDTV recorder • Internet Video • Video Conferences • Others

  23. MPEG-1 : a standard for storage and retrieval of moving pictures and audio on storage media MPEG-2 : a standard for digital television MPEG-4 : a standard for multimedia applications MPEG-7 : a content representation standard for information search MPEG-21: offers metadata information for audio and video files MPEG Overview

  24. MPEG-2 Overview • Extends video & audio compression of MPEG-1 - Substantially reduces bandwidth required for high-quality transmissions - Optimizes balance between resolution (quality) and bandwidth (speed)

  25. MPEG-2 Video Compression Overview • Video stream • Group of Pictures (GOP) • I-frames: can be reconstructed without any reference to other frames • P-frames: forward predicted from last I-frame and P-frames • B-frames: forward and backward predicted

  26. MPEG-2 Video Compression Overview • Compression: Eliminating Redundancies • Spatial Redundancy • Pixels are replicated within a single frame of video • Temporal Redundancy • Consecutive frames of video display images of the same scene

  27. MPEG-2 Video Compression Overview Four Video Compression Techniques: 1. Pre-processing 2. Temporal Prediction 3. Motion Compensation 4. Quantization

  28. MPEG-2 Video Compression Overview • Pre-processing • Filters out unnecessary information • Information that is difficult to encode • Not an important component of human visual perception

  29. MPEG-2 Video Compression Overview • Temporal Prediction: • Uses the mathematical algorithm Discrete Cosine Transform (DCT) to: • Divide each frame into 8X8 blocks of pixels • Reorganize residual differences between frames • Encode each block separately

  30. MPEG-2 Video Compression Overview

  31. MPEG-2 Video Compression Overview

  32. MPEG-2 Video Compression Overview

  33. I-Frame Independently reconstructed P-Frame Forward predicted from the last I-Frame or P-Frame B-Frame forward predicted and backward predicted from the last/next I-frame or P-frame Motion Compensation

  34. Motion Compensation contd… • In general, we speak of motion of objects in 3-D real world. 􀁺 Here, we are concerned with the "projected motion" of 3-D objects onto the 2-D plane of an imaging sensor. 􀁺 By motion estimation, we mean the estimation of the displacement (or velocity) of image structures from one frame to another in a time sequence of 2-D images. • 􀁺 In the literature, this projected motion is referred to as "apparent motion", "2-D image motion", or "optical flow".

  35. MPEG-2 Video Compression Overview • Quantization: • Refers to DCT coefficients • Removes subjective redundancy • Controls compression factor • Converts coefficients into even smaller numbers

  36. Multimedia Communications Webcasting Broadcasting Video on Demand Interactive Digital Media Telecommunications Mobile communications MPEG-2 Video Compression Overview Where It Is Used:

  37. MPEG Transport Streams

  38. CONCLUSION: Improving television with larger screens and better resolution requires a huge increase in transmission bit rates. The bit rates are, however, limited by the available broadcast spectrum or network connection. The only resource is lossy image compression, most commonly JPEG, MPEG-2. “Lossy” by name and lossy by nature. Because random noise in the images is interpreted as “movement” any increase in noise increases the transmissions. The cleaner the images and the less noise, the lower the average transmission bandwidth. It is therefore cheaper to transmit high quality images and videos.

  39. Digital Video Compression Thank You