DOCSLIB.ORG

MPEG-1 Audio

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
MPEG-1 Audio 18-899 Special Topics in Signal Processing Multimedia Communications: Coding, Systems, and Networking Prof. Tsuhan Chen [email protected] Lecture 8 MPEG-1 Audio 1 MPEG-1 Audio • Outline – Background – Psychoacoustics – Subband coding – Layer I and II – Layer III – Frame structure and packetization 18-899/Spring 1998/Chen MPEG-1 Audio • ISO/IEC 11172-3 (1988~1991) • First high quality audio compression standard • CD quality two-channel audio at 256 kbits/s – CD: 44.1 kHz × 16 bits × 2 = 1.411 Mbits/s Frequency Sampling Bits per Raw Bitrate Band (Hz) Rate Sample Telephone 300~3400 8 8 64 Speech Wideband 50~7000 16 8 128 Speech Mediumband 10~11000 24 16 384 Audio Wideband 10~22000 48 16 768 Audio 18-899/Spring 1998/Chen 2 Quality Demonstration • MPEG-1 Audio (Layer II) – Stereo 44.1 kHz at 64 kbits/s – Stereo 44.1 kHz at 128 kbits/s – Stereo 44.1 kHz at 192 kbits/s – Stereo 44.1 kHz at 256 kbits/s 18-899/Spring 1998/Chen Psychoacoustics • Threshold in quiet 18-899/Spring 1998/Chen 3 Frequency Masking 18-899/Spring 1998/Chen Temporal Masking Post-Masking: 50~200ms Also Pre-Masking (much shorter) 18-899/Spring 1998/Chen 4 Encoder Block Diagram PCM encoded audio samples 32, 44.1, 48 kHz bitstream quantizer frame mapping and packing coding psychoacoustic model 11172-3 Encoder ancillary data 18-899/Spring 1998/Chen Decoder Block Diagram PCM encoded audio samples bitstream 32, 44.1, 48 kHz frame inverse reconstruction unpacking mapping 11172-3 Decoder ancillary data 18-899/Spring 1998/Chen 5 Mapping: Subband Coding H1(z) M Q M F1(z) H (z) 2 M Q M F2(z) HM(z) M Q M FM(z) Analysis Synthesis Filterbank Filterbank • Critical downsampling • Q should be based on signal-to-masking ratio (SMR) • Ear’s critical bands are not uniform, but logarithmic 18-899/Spring 1998/Chen Polyphase Filterbank M M z -1 z M M E(z) R(z) ... ... ... z -1 z M M • Alias cancellation and perfect reconstruction 18-899/Spring 1998/Chen 6 Layers • Increasing complexity, delay, and quality • Layer I ~384 kbits/s for perceptually lossless quality (4:1) • Layer II ~192 kbits/s for perceptually lossless quality (8:1) • Layer III ~128 kbits/s for perceptually lossless quality (12:1) 18-899/Spring 1998/Chen Layer I and II Encoder 32 Analysis Scaler & Filterbank Quantizer Mux Masking Dynamic Coder FFT Threshold Bit Generator Allocator 18-899/Spring 1998/Chen 7 Block-Based Coding 12 12 12 Analysis Filterbank ... Layer I Layer II • 12 samples for Layer I, 36 samples for Layer II • Block companding: Each block normalized by scalefactor • For Layer II, up to 3 scalefactors, with 2-bit scalefactor select • Each block receives one bit allocation Layer III Encoder 6 or 18 with overlap Analysis MDCT Scaler & Huffman Quantizer Coding Filterbank Mux Masking Coding FFT Threshold Generator 18-899/Spring 1998/Chen 8 New Features in Layer III • Modified DCT (MDCT) – DCT with overlap – Long/short window switching • Short for better temporal resolution (to prevent pre-echoes) • Long for better frequency resolution • Nonuniform quantization • Entropy coding – Run-length and Huffman coding • Bit reservoir (buffer) 18-899/Spring 1998/Chen Frame Structure Header Info Side Info Subband Sanples Aux Data • Header info: Sync bits, system info, CRC (cyclic redundancy code) • Side info: bit allocation, scalefactor, (and scalefactor select for Layer II and III) • Subband samples: 32 × 12 for Layer I, 32 × 36 for Layer II and III • Packetization: 4-byte header, 184-byte payload 18-899/Spring 1998/Chen 9 Stereo Redundancy Coding • Four modes: mono, stereo, dual with two separate channel, joint stereo • In joint stereo mode – Human stereo perception > 2kHz is based on envelope – Intensity stereo coding > 2kHz • Encode (L + R) • Assign independent left- and right- scalefactors • Layer III supports (L+R) and (L–R) coding 18-899/Spring 1998/Chen References – Peter Noll, “MPEG digital audio coding,” IEEE Signal Processing Magazine, Sept. 1997, pp. 59-81 – D. Pan, “A tutorial on MPEG/Audio compression,” IEEE Trans. on Multimedia, vol. 2, no. 2, 1995, pp. 60-74 18-899/Spring 1998/Chen 10.
Load more

Recommended publications
  • A Multidimensional Companding Scheme for Source Coding Witha Perceptually Relevant Distortion Measure
    A MULTIDIMENSIONAL COMPANDING SCHEME FOR SOURCE CODING WITHA PERCEPTUALLY RELEVANT DISTORTION MEASURE J. Crespo, P. Aguiar R. Heusdens Department of Electrical and Computer Engineering Department of Mediamatics Instituto Superior Técnico Technische Universiteit Delft Lisboa, Portugal Delft, The Netherlands ABSTRACT coding mechanisms based on quantization, where less percep- tually important information is quantized more roughly and In this paper, we develop a multidimensional companding vice-versa, and lossless coding of the symbols emitted by the scheme for the preceptual distortion measure by S. van de Par quantizer to reduce statistical redundancy. In the quantization [1]. The scheme is asymptotically optimal in the sense that process of the (en)coder, it is desirable to quantize the infor- it has a vanishing rate-loss with increasing vector dimension. mation extracted from the signal in a rate-distortion optimal The compressor operates in the frequency domain: in its sim- sense, i.e., in a way that minimizes the perceptual distortion plest form, it pointwise multiplies the Discrete Fourier Trans- experienced by the user subject to the constraint of a certain form (DFT) of the windowed input signal by the square-root available bitrate. of the inverse of the masking threshold, and then goes back into the time domain with the inverse DFT. The expander is Due to its mathematical tractability, the Mean Square based on numerical methods: we do one iteration in a fixed- Error (MSE) is the elected choice for the distortion measure point equation, and then we fine-tune the result using Broy- in many coding applications. In audio coding, however, the den’s method.
    [Show full text]
  • XAPP616 "Huffman Coding" V1.0
    Product Obsolete/Under Obsolescence Application Note: Virtex Series R Huffman Coding Author: Latha Pillai XAPP616 (v1.0) April 22, 2003 Summary Huffman coding is used to code values statistically according to their probability of occurence. Short code words are assigned to highly probable values and long code words to less probable values. Huffman coding is used in MPEG-2 to further compress the bitstream. This application note describes how Huffman coding is done in MPEG-2 and its implementation. Introduction The output symbols from RLE are assigned binary code words depending on the statistics of the symbol. Frequently occurring symbols are assigned short code words whereas rarely occurring symbols are assigned long code words. The resulting code string can be uniquely decoded to get the original output of the run length encoder. The code assignment procedure developed by Huffman is used to get the optimum code word assignment for a set of input symbols. The procedure for Huffman coding involves the pairing of symbols. The input symbols are written out in the order of decreasing probability. The symbol with the highest probability is written at the top, the least probability is written down last. The least two probabilities are then paired and added. A new probability list is then formed with one entry as the previously added pair. The least symbols in the new list are then paired. This process is continued till the list consists of only one probability value. The values "0" and "1" are arbitrarily assigned to each element in each of the lists. Figure 1 shows the following symbols listed with a probability of occurrence where: A is 30%, B is 25%, C is 20%, D is 15%, and E = 10%.
    [Show full text]
  • Image Compression Through DCT and Huffman Coding Technique
    International Journal of Current Engineering and Technology E-ISSN 2277 – 4106, P-ISSN 2347 – 5161 ©2015 INPRESSCO®, All Rights Reserved Available at http://inpressco.com/category/ijcet Research Article Image Compression through DCT and Huffman Coding Technique Rahul Shukla†* and Narender Kumar Gupta† †Department of Computer Science and Engineering, SHIATS, Allahabad, India Accepted 31 May 2015, Available online 06 June 2015, Vol.5, No.3 (June 2015) Abstract Image compression is an art used to reduce the size of a particular image. The goal of image compression is to eliminate the redundancy in a file’s code in order to reduce its size. It is useful in reducing the image storage space and in reducing the time needed to transmit the image. Image compression is more significant for reducing data redundancy for save more memory and transmission bandwidth. An efficient compression technique has been proposed which combines DCT and Huffman coding technique. This technique proposed due to its Lossless property, means using this the probability of loss the information is lowest. Result shows that high compression rates are achieved and visually negligible difference between compressed images and original images. Keywords: Huffman coding, Huffman decoding, JPEG, TIFF, DCT, PSNR, MSE 1. Introduction that can compress almost any kind of data. These are the lossless methods they retain all the information of 1 Image compression is a technique in which large the compressed data. amount of disk space is required for the raw images However, they do not take advantage of the 2- which seems to be a very big disadvantage during dimensional nature of the image data.
    [Show full text]
  • The Strengths and Weaknesses of Different Image Compression Methods Samuel Teare and Brady Jacobson Lossy Vs Lossless
    The Strengths and Weaknesses of Different Image Compression Methods Samuel Teare and Brady Jacobson Lossy vs Lossless Lossy compression reduces a file size by permanently removing parts of the data that may be redundant or not as noticeable. Lossless compression guarantees the original data can be recovered or decompressed from the compressed file. PNG Compression PNG Compression consists of three parts: 1. Filtering 2. LZ77 Compression Deflate Compression 3. Huffman Coding Filtering Five types of Filters: 1. None - No filter 2. Sub - difference between this byte and the byte to its left a. Sub(x) = Original(x) - Original(x - bpp) 3. Up - difference between this byte and the byte above it a. Up(x) = Original(x) - Above(x) 4. Average - difference between this byte and the average of the byte to the left and the byte above. a. Avg(x) = Original(x) − (Original(x-bpp) + Above(x))/2 5. Paeth - Uses the byte to the left, above, and above left. a. The nearest of the left, above, or above left to the estimate is the Paeth Predictor b. Paeth(x) = Original(x) - Paeth Predictor(x) Paeth Algorithm Estimate = left + above - above left Distance to left = Absolute(estimate - left) Distance to above = Absolute(estimate - above) Distance to above left = Absolute(estimate - above left) The byte with the smallest distance is the Paeth Predictor LZ77 Compression LZ77 Compression looks for sequences in the data that are repeated. LZ77 uses a sliding window to keep track of previous bytes. This is then used to compress a group of bytes that exhibit the same sequence as previous bytes.
    [Show full text]
  • An Optimized Huffman's Coding by the Method of Grouping
    An Optimized Huffman’s Coding by the method of Grouping Gautam.R Dr. S Murali Department of Electronics and Communication Engineering, Professor, Department of Computer Science Engineering Maharaja Institute of Technology, Mysore Maharaja Institute of Technology, Mysore [email protected] [email protected] Abstract— Data compression has become a necessity not only the 3 depending on the size of the data. Huffman's coding basically in the field of communication but also in various scientific works on the principle of frequency of occurrence for each experiments. The data that is being received is more and the symbol or character in the input. For example we can know processing time required has also become more. A significant the number of times a letter has appeared in a text document by change in the algorithms will help to optimize the processing processing that particular document. After which we will speed. With the invention of Technologies like IoT and in assign a variable string to the letter that will represent the technologies like Machine Learning there is a need to compress character. Here the encoding take place in the form of tree data. For example training an Artificial Neural Network requires structure which will be explained in detail in the following a lot of data that should be processed and trained in small paragraph where encoding takes place in the form of binary interval of time for which compression will be very helpful. There tree. is a need to process the data faster and quicker. In this paper we present a method that reduces the data size.
    [Show full text]
  • Arithmetic Coding
    Arithmetic Coding Arithmetic coding is the most efficient method to code symbols according to the probability of their occurrence. The average code length corresponds exactly to the possible minimum given by information theory. Deviations which are caused by the bit-resolution of binary code trees do not exist. In contrast to a binary Huffman code tree the arithmetic coding offers a clearly better compression rate. Its implementation is more complex on the other hand. In arithmetic coding, a message is encoded as a real number in an interval from one to zero. Arithmetic coding typically has a better compression ratio than Huffman coding, as it produces a single symbol rather than several separate codewords. Arithmetic coding differs from other forms of entropy encoding such as Huffman coding in that rather than separating the input into component symbols and replacing each with a code, arithmetic coding encodes the entire message into a single number, a fraction n where (0.0 ≤ n < 1.0) Arithmetic coding is a lossless coding technique. There are a few disadvantages of arithmetic coding. One is that the whole codeword must be received to start decoding the symbols, and if there is a corrupt bit in the codeword, the entire message could become corrupt. Another is that there is a limit to the precision of the number which can be encoded, thus limiting the number of symbols to encode within a codeword. There also exist many patents upon arithmetic coding, so the use of some of the algorithms also call upon royalty fees. Arithmetic coding is part of the JPEG data format.
    [Show full text]
  • (A/V Codecs) REDCODE RAW (.R3D) ARRIRAW
    What is a Codec? Codec is a portmanteau of either "Compressor-Decompressor" or "Coder-Decoder," which describes a device or program capable of performing transformations on a data stream or signal. Codecs encode a stream or signal for transmission, storage or encryption and decode it for viewing or editing. Codecs are often used in videoconferencing and streaming media solutions. A video codec converts analog video signals from a video camera into digital signals for transmission. It then converts the digital signals back to analog for display. An audio codec converts analog audio signals from a microphone into digital signals for transmission. It then converts the digital signals back to analog for playing. The raw encoded form of audio and video data is often called essence, to distinguish it from the metadata information that together make up the information content of the stream and any "wrapper" data that is then added to aid access to or improve the robustness of the stream. Most codecs are lossy, in order to get a reasonably small file size. There are lossless codecs as well, but for most purposes the almost imperceptible increase in quality is not worth the considerable increase in data size. The main exception is if the data will undergo more processing in the future, in which case the repeated lossy encoding would damage the eventual quality too much. Many multimedia data streams need to contain both audio and video data, and often some form of metadata that permits synchronization of the audio and video. Each of these three streams may be handled by different programs, processes, or hardware; but for the multimedia data stream to be useful in stored or transmitted form, they must be encapsulated together in a container format.
    [Show full text]
  • Image Compression Using Discrete Cosine Transform Method
    Qusay Kanaan Kadhim, International Journal of Computer Science and Mobile Computing, Vol.5 Issue.9, September- 2016, pg. 186-192 Available Online at www.ijcsmc.com International Journal of Computer Science and Mobile Computing A Monthly Journal of Computer Science and Information Technology ISSN 2320–088X IMPACT FACTOR: 5.258 IJCSMC, Vol. 5, Issue. 9, September 2016, pg.186 – 192 Image Compression Using Discrete Cosine Transform Method Qusay Kanaan Kadhim Al-Yarmook University College / Computer Science Department, Iraq [email protected] ABSTRACT: The processing of digital images took a wide importance in the knowledge field in the last decades ago due to the rapid development in the communication techniques and the need to find and develop methods assist in enhancing and exploiting the image information. The field of digital images compression becomes an important field of digital images processing fields due to the need to exploit the available storage space as much as possible and reduce the time required to transmit the image. Baseline JPEG Standard technique is used in compression of images with 8-bit color depth. Basically, this scheme consists of seven operations which are the sampling, the partitioning, the transform, the quantization, the entropy coding and Huffman coding. First, the sampling process is used to reduce the size of the image and the number bits required to represent it. Next, the partitioning process is applied to the image to get (8×8) image block. Then, the discrete cosine transform is used to transform the image block data from spatial domain to frequency domain to make the data easy to process.
    [Show full text]
  • Hybrid Compression Using DWT-DCT and Huffman Encoding Techniques for Biomedical Image and Video Applications
    Available Online at www.ijcsmc.com International Journal of Computer Science and Mobile Computing A Monthly Journal of Computer Science and Information Technology ISSN 2320–088X IJCSMC, Vol. 2, Issue. 5, May 2013, pg.255 – 261 RESEARCH ARTICLE Hybrid Compression Using DWT-DCT and Huffman Encoding Techniques for Biomedical Image and Video Applications K.N. Bharath 1, G. Padmajadevi 2, Kiran 3 1Department of E&C Engg, Malnad College of Engineering, VTU, India 2Associate Professor, Department of E&C Engg, Malnad College of Engineering, VTU, India 3Department of E&C Engg, Malnad College of Engineering, VTU, India 1 [email protected]; 2 [email protected]; 3 [email protected] Abstract— Digital image and video in their raw form require an enormous amount of storage capacity. Considering the important role played by digital imaging and video, it is necessary to develop a system that produces high degree of compression while preserving critical image/video information. There is various transformation techniques used for data compression. Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT) are the most commonly used transformation. DCT has high energy compaction property and requires less computational resources. On the other hand, DWT is multi resolution transformation. In this work, we propose a hybrid DWT-DCT, Huffman algorithm for image and video compression and reconstruction taking benefit from the advantages of both algorithms. The algorithm performs the Discrete Cosine Transform (DCT) on the Discrete Wavelet Transform (DWT) coefficients. Simulations have been conducted on several natural, benchmarks, medical and endoscopic images. Several high definition and endoscopic videos have also been used to demonstrate the advantage of the proposed scheme.
    [Show full text]
  • MC14SM5567 PCM Codec-Filter
    Product Preview Freescale Semiconductor, Inc. MC14SM5567/D Rev. 0, 4/2002 MC14SM5567 PCM Codec-Filter The MC14SM5567 is a per channel PCM Codec-Filter, designed to operate in both synchronous and asynchronous applications. This device 20 performs the voice digitization and reconstruction as well as the band 1 limiting and smoothing required for (A-Law) PCM systems. DW SUFFIX This device has an input operational amplifier whose output is the input SOG PACKAGE CASE 751D to the encoder section. The encoder section immediately low-pass filters the analog signal with an RC filter to eliminate very-high-frequency noise from being modulated down to the pass band by the switched capacitor filter. From the active R-C filter, the analog signal is converted to a differential signal. From this point, all analog signal processing is done differentially. This allows processing of an analog signal that is twice the amplitude allowed by a single-ended design, which reduces the significance of noise to both the inverted and non-inverted signal paths. Another advantage of this differential design is that noise injected via the power supplies is a common mode signal that is cancelled when the inverted and non-inverted signals are recombined. This dramatically improves the power supply rejection ratio. After the differential converter, a differential switched capacitor filter band passes the analog signal from 200 Hz to 3400 Hz before the signal is digitized by the differential compressing A/D converter. The decoder accepts PCM data and expands it using a differential D/A converter. The output of the D/A is low-pass filtered at 3400 Hz and sinX/X compensated by a differential switched capacitor filter.
    [Show full text]
  • Implementation of a Fast Mpeg-2 Compliant Huffman Decoder
    IMPLEMENTATION OF A FAST MPEG-2 COMPLIANT HUFFMAN DECODER Mikael Karlsson Rudberg ([email protected]) and Lars Wanhammar ([email protected]) Department of Electrical Engineering, Linköping University, S-581 83 Linköping, Sweden Tel: +46 13 284059; fax: +46 13 139282 ABSTRACT 2. HUFFMAN DECODER In this paper a 100 Mbit/s Huffman decoder Huffman decoding can be performed in a numerous implementation is presented. A novel approach ways. One common principle is to decode the where a parallel decoding of data mixed with a incoming bit stream in parallel [3, 4]. The serial input has been used. The critical path has simplified decoding process is described below: been reduced and a significant increase in throughput is achieved. The decoder is aimed at 1. Feed a symbol decoder and a length the MPEG-2 Video decoding standard and has decoder with M bits, where M is the length therefore been designed to meet the required of the longest code word. performance. 2. The symbol decoder maps the input vector to the corresponding symbol. A length 1. INTRODUCTION decoder will at the same time find the length of the input vector. Huffman coding is a lossless compression 3. The information from the length decoder is technique often used in combination with other used in the input buffer to fill up the buffer lossy compression methods, in for instance digital again (with between one and M bits, figure video and audio applications. The Huffman coding 1). method uses codes with different lengths, where symbols with high probability are assigned shorter The problem with this solution is the long critical codes than symbols with lower probability.
    [Show full text]
  • TMS320C6000 U-Law and A-Law Companding with Software Or The
    Application Report SPRA634 - April 2000 TMS320C6000t µ-Law and A-Law Companding with Software or the McBSP Mark A. Castellano Digital Signal Processing Solutions Todd Hiers Rebecca Ma ABSTRACT This document describes how to perform data companding with the TMS320C6000 digital signal processors (DSP). Companding refers to the compression and expansion of transfer data before and after transmission, respectively. The multichannel buffered serial port (McBSP) in the TMS320C6000 supports two companding formats: µ-Law and A-Law. Both companding formats are specified in the CCITT G.711 recommendation [1]. This application report discusses how to use the McBSP to perform data companding in both the µ-Law and A-Law formats. This document also discusses how to perform companding of data not coming into the device but instead located in a buffer in processor memory. Sample McBSP setup codes are provided for reference. In addition, this application report discusses µ-Law and A-Law companding in software. The appendix provides software companding codes and a brief discussion on the companding theory. Contents 1 Design Problem . 2 2 Overview . 2 3 Companding With the McBSP. 3 3.1 µ-Law Companding . 3 3.1.1 McBSP Register Configuration for µ-Law Companding. 4 3.2 A-Law Companding. 4 3.2.1 McBSP Register Configuration for A-Law Companding. 4 3.3 Companding Internal Data. 5 3.3.1 Non-DLB Mode. 5 3.3.2 DLB Mode . 6 3.4 Sample C Functions. 6 4 Companding With Software. 6 5 Conclusion . 7 6 References . 7 TMS320C6000 is a trademark of Texas Instruments Incorporated.
    [Show full text]