![2/3/2023
Siddalinga /
9
Lempel-Ziv Encoding
• The basic idea is never to copy a sequence of bytes to the
output stream that the encoder has seen before
• This encoding is used in the UNIX compress utility
Algorithm :
1. Initialize the code table with the elements of the alphabet,one entry for each character.
2. Initialize the scan window as empty : [ ].
3. Accept the next chracter K from the input stream and concatenate it with the scan window : [w]k.
4. Do we have an entry for [w]k in the code table ?.
-If yes, integrate K into the scan window : w1 := [wK] and goto 3.
-If no, add [w]K as a new entry to the code table ,write the index of [w] to the output stream,set
[w] :=[K] and goto 3.
5. When the end of the input stream is reached process [w] from left to right,choosing the longest
possible substrings from the code.](https://image.slidesharecdn.com/siddu02-230203075216-0864ec3a/85/siddu02-ppt-9-320.jpg)
siddu02.ppt
- 1. 2/3/2023 Siddalinga / 1 Fundamentals of Video Compression • Introduction to Digital Video • Basic Compression Techniques • Still Image Compression Techniques - JPEG • Video Compression
- 2. 2/3/2023 Siddalinga / 2 Introduction to Digital Video • Video is a stream of data composed of discrete frames, containing both audio and pictures • Continuous motion produced at a frame rate of 15 fps or higher • Traditional movies run at 24 fps • TV standard in USA (NTSC) uses 30 fps
- 3. 2/3/2023 Siddalinga / 3 Motivation for Video Compression Main reasons for compression of digital video: a) large storage requirement (a 30 minute video may require 50 GB of storage!!!) b) limited network bandwidth for real time video transmission
- 4. 2/3/2023 Siddalinga / 4 Compression Constraints •Quality •Compression Rate •Complexity •Delay
- 5. 2/3/2023 Siddalinga / 5 Multimedia Compression Basics • Compression is a process where a collection of algorithms, and techniques replace the original pixel-related information with more compact mathematical description • Two basic types of compression are lossless and lossy
- 6. 2/3/2023 Siddalinga / 6 Lossless vs. Lossy Compression • In lossless compression, data is not altered or lost in the process of compression or decompression • Some examples of lossless standards are: — Run-Length Encoding — Dynamic Pattern Substitution - Lampel-Ziv Encoding — Huffman Encoding • Lossy compression is used for compressing audio, pictures, video • Some examples are: — JPEG — MPEG — H.261 (Px64) Video Coding Algorithm
- 7. 2/3/2023 Siddalinga / 7 Run-length Encoding • Simplest and earliest data compression scheme developed • Sampled images and audio and video data streams often contain sequences of identical bytes • by replacing these sequences with the byte pattern to be repeated and providing the number of its occurrence,data can be reduced substantially
- 8. 2/3/2023 Siddalinga / 8 Dynamic Pattern Substitution • When we have no prior knowledge of the sequences of symbols occurring frequently • While encoding the stream, a code table must be constructed
- 9. 2/3/2023 Siddalinga / 9 Lempel-Ziv Encoding • The basic idea is never to copy a sequence of bytes to the output stream that the encoder has seen before • This encoding is used in the UNIX compress utility Algorithm : 1. Initialize the code table with the elements of the alphabet,one entry for each character. 2. Initialize the scan window as empty : [ ]. 3. Accept the next chracter K from the input stream and concatenate it with the scan window : [w]k. 4. Do we have an entry for [w]k in the code table ?. -If yes, integrate K into the scan window : w1 := [wK] and goto 3. -If no, add [w]K as a new entry to the code table ,write the index of [w] to the output stream,set [w] :=[K] and goto 3. 5. When the end of the input stream is reached process [w] from left to right,choosing the longest possible substrings from the code.
- 10. 2/3/2023 Siddalinga / 10 Huffman Encoding • David Huffman proposed an algorithm for constructing a variable-length code, an Optimal algorithm • Winzip the most popular compression utility uses Huffman Algorithm
- 11. 2/3/2023 Siddalinga / 11 Still Image Compression - JPEG • Defined by Joint Photographic Experts Group • Released as an ISO standard for still color and gray-scale images • Provides four modes of operation: — Sequential (each pixel is traversed only once) — progressive (image gets progressively sharper) — Hierarchical (image compressed to multiple resolutions) — lossless (full detail at selected resolution)
- 12. 2/3/2023 Siddalinga / 12 Definitions in the JPEG Standard Three levels of definition: • Baseline system (every codec must implement it) • Extended system (methods to extend the baseline system) • Special lossless function (ensures lossless compression/ decompression)
- 13. 2/3/2023 Siddalinga / 13 Sequential JPEG Encoder and Decoder Forward Discrete Cosine Transform Quantizer Entropy Encoder Table Specification Table Specification Entropy Decoder Dequantizer Inverse DCT Table Specification Table Specification Source Image Data Reconstructed Image Data Compressed Image Data Compressed Image Data 8x8 blocks
- 14. 2/3/2023 Siddalinga / 14 Benefits Provided by DCT • DCT is proven to be optimal transform for large classes of images • DCT is an orthogonal transform: it allows conversion of the spatial representation an 8x8image to the frequency domain therefore reducing the number of data points • DCT coefficients are easily quantized to achieve good compression • DCT algorithm is efficient and easy to implement • DCT algorithm is symmetrical
- 15. 2/3/2023 Siddalinga / 15 Quantization • Quantization is a process that attempts to determine what information can be safely discarded without a significant loss in visual fidelity (“lossy” stage) • Based on a set of quantization tables derived from empirical experimentation
- 16. 2/3/2023 Siddalinga / 16 Video Compression Utilizes two basic compression techniques: • Interframe compression — compression between frames — designed to minimize data redundancy in successive pictures(Temporal redundancy) • Intraframe compression — occurs within individual frames — designed to minimize the duplication of data in each picture(Spatial Redundancy)
- 17. 2/3/2023 Siddalinga / 17 Classification of Scalable Video Compression Techniques • DCT-based schemes — MPEG1 — MPEG2 — H.261 — H.263 • Wavelet/sub-band • Fractal-based • Image segmentation/region based — MPEG4
- 18. 2/3/2023 Siddalinga / 18 Various MPEG Standards • MPEG-1 — 320x240 full-motion video — 1.5 Mb/s • MPEG-2 — higher resolution and transmission rate 3-15Mb/s — defines different levels (profiles) for scalability • MPEG-4 — full-motion video at low bitrate (9-40 Kbps) — intended for interactive multimedia, video telephony
- 19. 2/3/2023 Siddalinga / 19 MPEG Compression Standards Implements both intraframe and interframe coding •Intraframe( Spatial Redundancy) is DCT-based and very similar to JPEG • Interframe(Temporal Redundancy) uses block-based motion compensation — utilized for reducing temporal redundancy
- 20. 2/3/2023 Siddalinga / 20 MPEG Picture Types Three types of pictures: • Intrapictures (I) • Unidirectional predicted pictures (P) • Bidirectional predicted pictures (B) Grouped together (typically 12 pictures) in GOPs
- 21. 2/3/2023 Siddalinga / 21 Motion Compression for Coding MPEG I B B B P B B B I Bidirectional prediction B=f(I,P) Bidirectional prediction B=f(I,P) Forward prediction P=f(I)
- 22. 2/3/2023 Siddalinga / 22 H.261 (Px64) • H.261 was designed for datarates which are multiples of 64Kbit/s, and is sometimes called p x 64Kbit/s (p is in the range 1-30). •These datarates suit ISDN lines, for which this video codec was designed for • Intended for videophone and video conferencing systems
- 23. 2/3/2023 Siddalinga / 23 H.263 Standard • The development of modems allowing transmission in the range of 28-33 kbps paved the way for the development of an improved version of H.261 • It was designed for low bitrate communication , however this limitationhas now been removed • It is expected that H.263 will replace H.261
- 24. 2/3/2023 Siddalinga / 24 Non-DCT Based Compression Techniques •Image Compression by Fractals •Image compression by Wavelets