Fundamentals Series Defining Quality

Total Page:16

File Type:pdf, Size:1020Kb

Fundamentals Series Defining Quality Fundamentals Series Defining Quality © Polycom, Inc. All rights reserved. Fundamentals Series Signals H.323 Analog vs. Digital SIP Network Defining Quality Communication I Network Standards Communication II © Polycom, Inc. All rights reserved. 2 Welcome to Defining Quality, the third module in the Polycom Fundamentals series. This module is approximately 10 minutes long. Introduction © Polycom, Inc. All rights reserved. 3 In order to understand how videoconferencing works it’s important to understand the underlying technologies at work behind the scenes. In this short module we will talk some more about digital video, how we compress it and the effects that has on the quality of the video we see. We won’t get too in depth here, the goal is just to give you an understanding of how these basic technologies work. Remember that to make a digital signal, we sample the analog waveform, meaning that we take a measurement of the waveform a number of times per second (the sample rate). Each sample is saved using a number of bits; the exact number is known as the bit depth. Compression © Polycom, Inc. All rights reserved. 4 So, once we have a digital representation of our analog waveform, how does this get across the network quickly enough to give us a real-time conference? Well, a related concept to sampling is compression. Digital compression comes into play when we want to use a particular bit depth (say 16-bit) and then send that information using less than the originally sampled data (say 8 bits). We “compress” the information by using algorithms (mathematical calculations) to manipulate the data and then to recreate that information in an acceptable way at the other end. We use fewer bits to represent the larger numbers with minimal loss of quality. It’s tricky but compression can become really important when you’re trying to send a lot of information over a limited amount of bandwidth and need it all to arrive quickly. μ-law and A-law © Polycom, Inc. All rights reserved. 5 Two examples of compression used in digital audio are the μ-law (mu law or u-law) and A- law algorithms. These are what are known as “companding” algorithms. They handle the compressing and expanding of digital information (giving us comp-anding). By segmenting the signal range and treating each a little differently (by having more steps in lower ranges than in the higher ranges) we can use fewer values for high frequencies and more for the mid range frequencies, the ones in the center of the human hearing range. This makes the mid range sounds more accurate than the higher frequencies, but that’s okay because we really need more accuracy in the speech range for voice communications anyway. So, by using μ-law and A-law we can take samples at a 16 bit bit-depth (with 65,536 possible values for each sample, making it very accurate) and transmitting that information using only 8 bits of information (using 255 possible values). This is great since the primary information we’re worried about is carried in the mid ranges around the 1 kHz sound frequency. So μ-law and A-law focus on keeping more samples in that range and accepting lower quality at the higher and lower ends of the sound spectrum Audio Transmission © Polycom, Inc. All rights reserved. 6 Now let’s apply that to a digitized audio signal. If I have an original sound frequency range of 300 to 4000 Hz, and I sample that at 8000 samples per second, and I use 16 bits per sample to get the best audio quality, but then use the μ-law algorithm to compress that down to 8 bits for transmission… I end up with a 64,000 bits per second audio data stream. That gives me voice quality audio in a 64 kbps stream. 8000 samples per second transmitted using 8 bits = 64,000 bits per second. That happens to be the G.711 audio codec standard. How about that. A codec is just a word for a technique for coding and decoding a signal. Audio Codecs © Polycom, Inc. All rights reserved. 7 There are other audio codecs that are used in digital telephony and video conferencing. Some of the most common are listed here. They are all just built of different variations of sample rates, bit depths and compression algorithms. When we transmit these in our video conferencing call they are all sent across the network as voltage levels representing on/off (bits) in sequenced groups (bytes). There… simple isn’t it? Pixels © Polycom, Inc. All rights reserved. 8 Moving onto pictures, a digital picture is made up of pixels. Pixel is a shortened form of ‘picture element’, which is the smallest screen element in a display. The more pixels on the screen, the higher we say the resolution is. Here is a great image showing kind of how this works – when you look close up, like a low resolution image, you can see the pixels quite clearly. But when you view the whole image, like a higher resolution image, the smoother it is. Resolutions © Polycom, Inc. All rights reserved. 9 The resolution of the image is defined as the number of pixels across the screen by the number of lines on the screen. There are many, many standard resolutions, several of which you will be familiar with, such as VGA, which is 640 pixels by 480 lines. Each resolution has what we call an aspect ratio; the two most common are 4:3 (like an old television) or 16:9 (widescreen). Looking at the 640 x 480 example just given, you can see that it has a 4:3 aspect ratio, that is to say, if you split the 640 into 4, which gives 160, and then multiply that by 3, you get 480. This ties into video codecs as not all codecs support all resolutions, for example, the resolutions we describe as being ‘high definition’ require a specific codec to decode them. This means that even if you receive an HD signal, if you can’t decode it, you won’t get HD. Television Standards © Polycom, Inc. All rights reserved. 10 We will divert here momentarily to talk about television standards. Television wasn’t created in just one place and distributed around the world, and as a result, there were several competing systems for standard definition (4:3) TV that developed around the same time. The primary standards we see today are NTSC (National Television System Committee), PAL (Phase Alternating Line) and SECAM (Séquentiel couleur à mémoire, "Sequential Color with Memory”). You can see on the map where each is still used today. Each of these have slightly different ways in which they display video images on a monitor. There are two primary differences between them; the first is the number of lines that make up the picture, which as we know will help give us the resolution of the image, and the second is the number of times per second the image refreshes on the screen, which is known as the frame rate, and affects how smoothly the moving picture, well, moves. We don’t need to get into this any further, but it is worth knowing as when we move forward into discussing specific resolutions, you will find that some have been specified to NTSC standards, and some have been specified to PAL/SECAM standards. PAL and SECAM share the same resolution and frame rate so do not require separate resolutions, although they are not compatible due to the way they handle color signaling. Video Codecs © Polycom, Inc. All rights reserved. 11 There are several digital video compression codecs that are used today. In our video conferencing systems we commonly see three of them. These are H.261, H.263 and H.264. The primary things that differentiate them from each other fall into three categories: What resolutions does it support What frame rates does it support How much does it compress the video sequence to keep quality high and required bandwidth low Each codec does this a little differently, and although we will discuss this further, for now we are going to talk about what these three parameters mean to us, starting with resolution. We will concentrate here on resolutions commonly used in videoconferencing. SD Resolutions CIF SIF 4CIF 4SIF © Polycom, Inc. All rights reserved. 12 The Standard Definition, or SD, resolutions we will look at here all use the 4:3 aspect ratio. One of the first videoconferencing resolutions was CIF or Common International Format. It was first proposed in the H.261 standard back in 1988 as an easy way to convert PAL to NTSC and back. This is not to be confused with Source Input Format (SIF) which is a similar low resolution format but only for NTSC. Both of these also make resolutions by multiplying or dividing the base resolution. The most commonly seen of these is 4CIF and 4SIF, which multiply the base resolution by 2 for a higher quality. Doubling the resolution gives four times the quality; if this doesn’t seem to make sense, think of it this way; if I have a square and I double the length of both sides, how many of the originally sized squares can I fit inside the one which is twice the size? Yup, that would be four. HD Resolutions © Polycom, Inc. All rights reserved. 13 In comparison, HD resolutions are all widescreen, or 16:9 aspect ratio formats. The most common HD resolutions are shown here – 1280 x 720, and 1920 x 1080.
Recommended publications
  • PXC 550 Wireless Headphones
    PXC 550 Wireless headphones Instruction Manual 2 | PXC 550 Contents Contents Important safety instructions ...................................................................................2 The PXC 550 Wireless headphones ...........................................................................4 Package includes ..........................................................................................................6 Product overview .........................................................................................................7 Overview of the headphones .................................................................................... 7 Overview of LED indicators ........................................................................................ 9 Overview of buttons and switches ........................................................................10 Overview of gesture controls ..................................................................................11 Overview of CapTune ................................................................................................12 Getting started ......................................................................................................... 14 Charging basics ..........................................................................................................14 Installing CapTune .....................................................................................................16 Pairing the headphones ...........................................................................................17
    [Show full text]
  • Audio Coding for Digital Broadcasting
    Recommendation ITU-R BS.1196-7 (01/2019) Audio coding for digital broadcasting BS Series Broadcasting service (sound) ii Rec. ITU-R BS.1196-7 Foreword The role of the Radiocommunication Sector is to ensure the rational, equitable, efficient and economical use of the radio- frequency spectrum by all radiocommunication services, including satellite services, and carry out studies without limit of frequency range on the basis of which Recommendations are adopted. The regulatory and policy functions of the Radiocommunication Sector are performed by World and Regional Radiocommunication Conferences and Radiocommunication Assemblies supported by Study Groups. Policy on Intellectual Property Right (IPR) ITU-R policy on IPR is described in the Common Patent Policy for ITU-T/ITU-R/ISO/IEC referenced in Resolution ITU-R 1. Forms to be used for the submission of patent statements and licensing declarations by patent holders are available from http://www.itu.int/ITU-R/go/patents/en where the Guidelines for Implementation of the Common Patent Policy for ITU-T/ITU-R/ISO/IEC and the ITU-R patent information database can also be found. Series of ITU-R Recommendations (Also available online at http://www.itu.int/publ/R-REC/en) Series Title BO Satellite delivery BR Recording for production, archival and play-out; film for television BS Broadcasting service (sound) BT Broadcasting service (television) F Fixed service M Mobile, radiodetermination, amateur and related satellite services P Radiowave propagation RA Radio astronomy RS Remote sensing systems S Fixed-satellite service SA Space applications and meteorology SF Frequency sharing and coordination between fixed-satellite and fixed service systems SM Spectrum management SNG Satellite news gathering TF Time signals and frequency standards emissions V Vocabulary and related subjects Note: This ITU-R Recommendation was approved in English under the procedure detailed in Resolution ITU-R 1.
    [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]
  • A Multi-Frame PCA-Based Stereo Audio Coding Method
    applied sciences Article A Multi-Frame PCA-Based Stereo Audio Coding Method Jing Wang *, Xiaohan Zhao, Xiang Xie and Jingming Kuang School of Information and Electronics, Beijing Institute of Technology, 100081 Beijing, China; [email protected] (X.Z.); [email protected] (X.X.); [email protected] (J.K.) * Correspondence: [email protected]; Tel.: +86-138-1015-0086 Received: 18 April 2018; Accepted: 9 June 2018; Published: 12 June 2018 Abstract: With the increasing demand for high quality audio, stereo audio coding has become more and more important. In this paper, a multi-frame coding method based on Principal Component Analysis (PCA) is proposed for the compression of audio signals, including both mono and stereo signals. The PCA-based method makes the input audio spectral coefficients into eigenvectors of covariance matrices and reduces coding bitrate by grouping such eigenvectors into fewer number of vectors. The multi-frame joint technique makes the PCA-based method more efficient and feasible. This paper also proposes a quantization method that utilizes Pyramid Vector Quantization (PVQ) to quantize the PCA matrices proposed in this paper with few bits. Parametric coding algorithms are also employed with PCA to ensure the high efficiency of the proposed audio codec. Subjective listening tests with Multiple Stimuli with Hidden Reference and Anchor (MUSHRA) have shown that the proposed PCA-based coding method is efficient at processing stereo audio. Keywords: stereo audio coding; Principal Component Analysis (PCA); multi-frame; Pyramid Vector Quantization (PVQ) 1. Introduction The goal of audio coding is to represent audio in digital form with as few bits as possible while maintaining the intelligibility and quality required for particular applications [1].
    [Show full text]
  • Lossless Compression of Audio Data
    CHAPTER 12 Lossless Compression of Audio Data ROBERT C. MAHER OVERVIEW Lossless data compression of digital audio signals is useful when it is necessary to minimize the storage space or transmission bandwidth of audio data while still maintaining archival quality. Available techniques for lossless audio compression, or lossless audio packing, generally employ an adaptive waveform predictor with a variable-rate entropy coding of the residual, such as Huffman or Golomb-Rice coding. The amount of data compression can vary considerably from one audio waveform to another, but ratios of less than 3 are typical. Several freeware, shareware, and proprietary commercial lossless audio packing programs are available. 12.1 INTRODUCTION The Internet is increasingly being used as a means to deliver audio content to end-users for en­ tertainment, education, and commerce. It is clearly advantageous to minimize the time required to download an audio data file and the storage capacity required to hold it. Moreover, the expec­ tations of end-users with regard to signal quality, number of audio channels, meta-data such as song lyrics, and similar additional features provide incentives to compress the audio data. 12.1.1 Background In the past decade there have been significant breakthroughs in audio data compression using lossy perceptual coding [1]. These techniques lower the bit rate required to represent the signal by establishing perceptual error criteria, meaning that a model of human hearing perception is Copyright 2003. Elsevier Science (USA). 255 AU rights reserved. 256 PART III / APPLICATIONS used to guide the elimination of excess bits that can be either reconstructed (redundancy in the signal) orignored (inaudible components in the signal).
    [Show full text]
  • Improving Opus Low Bit Rate Quality with Neural Speech Synthesis
    Improving Opus Low Bit Rate Quality with Neural Speech Synthesis Jan Skoglund1, Jean-Marc Valin2∗ 1Google, San Francisco, CA, USA 2Amazon, Palo Alto, CA, USA [email protected], [email protected] Abstract learned representation set [11]. A typical WaveNet configura- The voice mode of the Opus audio coder can compress wide- tion requires a very high algorithmic complexity, in the order band speech at bit rates ranging from 6 kb/s to 40 kb/s. How- of hundreds of GFLOPS, along with a high memory usage to ever, Opus is at its core a waveform matching coder, and as the hold the millions of model parameters. Combined with the high rate drops below 10 kb/s, quality degrades quickly. As the rate latency, in the hundreds of milliseconds, this renders WaveNet reduces even further, parametric coders tend to perform better impractical for a real-time implementation. Replacing the di- than waveform coders. In this paper we propose a backward- lated convolutional networks with recurrent networks improved compatible way of improving low bit rate Opus quality by re- memory efficiency in SampleRNN [12], which was shown to be synthesizing speech from the decoded parameters. We compare useful for speech coding in [13]. WaveRNN [14] also demon- two different neural generative models, WaveNet and LPCNet. strated possibilities for synthesizing at lower complexities com- WaveNet is a powerful, high-complexity, and high-latency ar- pared to WaveNet. Even lower complexity and real-time opera- chitecture that is not feasible for a practical system, yet pro- tion was recently reported using LPCNet [15]. vides a best known achievable quality with generative models.
    [Show full text]
  • Cognitive Speech Coding Milos Cernak, Senior Member, IEEE, Afsaneh Asaei, Senior Member, IEEE, Alexandre Hyafil
    1 Cognitive Speech Coding Milos Cernak, Senior Member, IEEE, Afsaneh Asaei, Senior Member, IEEE, Alexandre Hyafil Abstract—Speech coding is a field where compression ear and undergoes a highly complex transformation paradigms have not changed in the last 30 years. The before it is encoded efficiently by spikes at the auditory speech signals are most commonly encoded with com- nerve. This great efficiency in information representation pression methods that have roots in Linear Predictive has inspired speech engineers to incorporate aspects of theory dating back to the early 1940s. This paper tries to cognitive processing in when developing efficient speech bridge this influential theory with recent cognitive studies applicable in speech communication engineering. technologies. This tutorial article reviews the mechanisms of speech Speech coding is a field where research has slowed perception that lead to perceptual speech coding. Then considerably in recent years. This has occurred not it focuses on human speech communication and machine because it has achieved the ultimate in minimizing bit learning, and application of cognitive speech processing in rate for transparent speech quality, but because recent speech compression that presents a paradigm shift from improvements have been small and commercial applica- perceptual (auditory) speech processing towards cognitive tions (e.g., cell phones) have been mostly satisfactory for (auditory plus cortical) speech processing. The objective the general public, and the growth of available bandwidth of this tutorial is to provide an overview of the impact has reduced requirements to compress speech even fur- of cognitive speech processing on speech compression and discuss challenges faced in this interdisciplinary speech ther.
    [Show full text]
  • Codec Is a Portmanteau of Either
    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]
  • Lossy Audio Compression Identification
    2018 26th European Signal Processing Conference (EUSIPCO) Lossy Audio Compression Identification Bongjun Kim Zafar Rafii Northwestern University Gracenote Evanston, USA Emeryville, USA [email protected] zafar.rafi[email protected] Abstract—We propose a system which can estimate from an compression parameters from an audio signal, based on AAC, audio recording that has previously undergone lossy compression was presented in [3]. The first implementation of that work, the parameters used for the encoding, and therefore identify the based on MP3, was then proposed in [4]. The idea was to corresponding lossy coding format. The system analyzes the audio signal and searches for the compression parameters and framing search for the compression parameters and framing conditions conditions which match those used for the encoding. In particular, which match those used for the encoding, by measuring traces we propose a new metric for measuring traces of compression of compression in the audio signal, which typically correspond which is robust to variations in the audio content and a new to time-frequency coefficients quantized to zero. method for combining the estimates from multiple audio blocks The first work to investigate alterations, such as deletion, in- which can refine the results. We evaluated this system with audio excerpts from songs and movies, compressed into various coding sertion, or substitution, in audio signals which have undergone formats, using different bit rates, and captured digitally as well lossy compression, namely MP3, was presented in [5]. The as through analog transfer. Results showed that our system can idea was to measure traces of compression in the signal along identify the correct format in almost all cases, even at high bit time and detect discontinuities in the estimated framing.
    [Show full text]
  • Speech Compression
    information Review Speech Compression Jerry D. Gibson Department of Electrical and Computer Engineering, University of California, Santa Barbara, CA 93118, USA; [email protected]; Tel.: +1-805-893-6187 Academic Editor: Khalid Sayood Received: 22 April 2016; Accepted: 30 May 2016; Published: 3 June 2016 Abstract: Speech compression is a key technology underlying digital cellular communications, VoIP, voicemail, and voice response systems. We trace the evolution of speech coding based on the linear prediction model, highlight the key milestones in speech coding, and outline the structures of the most important speech coding standards. Current challenges, future research directions, fundamental limits on performance, and the critical open problem of speech coding for emergency first responders are all discussed. Keywords: speech coding; voice coding; speech coding standards; speech coding performance; linear prediction of speech 1. Introduction Speech coding is a critical technology for digital cellular communications, voice over Internet protocol (VoIP), voice response applications, and videoconferencing systems. In this paper, we present an abridged history of speech compression, a development of the dominant speech compression techniques, and a discussion of selected speech coding standards and their performance. We also discuss the future evolution of speech compression and speech compression research. We specifically develop the connection between rate distortion theory and speech compression, including rate distortion bounds for speech codecs. We use the terms speech compression, speech coding, and voice coding interchangeably in this paper. The voice signal contains not only what is said but also the vocal and aural characteristics of the speaker. As a consequence, it is usually desired to reproduce the voice signal, since we are interested in not only knowing what was said, but also in being able to identify the speaker.
    [Show full text]
  • Video Source File Specifications
    Video Source File Specifications Limelight recommends the following specifications for all video source files. Adherence to these specifications will result in optimal playback quality and efficient uploading to your account. Edvance360 Best Practices Videos should be under 1 Gig for best results (download speed and mobile devices), but our limit per file is 2 Gig for Lessons MP4 is the optimum format for uploading videos Compress the video to resolution of 1024 x 768 Limelight does compress the video, but it's best if it's done on the original file A resolution is 1080p or less is recommended Recommended frame rate is 30fps Note: The maximum file size for Introduction Videos in Courses is 50MB. This is located in Courses > Settings > Details > Introduction Video. Ideal Source File In general, a source file that represents the following will produce the best results: MP4 file (H.264/ACC-LC) Fast Start (MOOV atom at the front of file) Progressive scan (no interlacing) Frame rate of 24 (23.98), 25, or 30 (29.97) fps A Bitrate between 5,000 - 8,000 Kbps 720p resolution Detailed Recommendations The table below provides detailed recommendations (CODECs, containers, Bitrates, resolutions, etc.) for all video source material uploaded to a Limelight Account: Source File Element Recommendations Video CODEC Recommended CODEC: H.264 Accepted but not Recommended: MPEG-1, MPEG-2, MPEG-4, VP6, VP5, H.263, Windows Media Video 7 (WMV1), Windows Media Video 8 (WMV2), Windows Media Video 9 (WMV3) Audio CODEC Recommended CODEC: AAC-LC Accepted but not Recommended: MP3, MP2, WMA, WMA Pro, PCM, WAV Container MP4 Source File Element Recommendations Fast-Start Make sure your source file is created with the 'MOOV atom' at the front of the file.
    [Show full text]
  • An Audio Codec for Multiple Generations Compression Without Loss of Perceptual Quality
    Frank Kurth An Audio Codec for Multiple Generations Compression without Loss of Perceptual Quality AN AUDIO CODEC FOR MULTIPLE GENERATIONS COMPRESSION WITHOUT LOSS OF PERCEPTUAL QUALITY FRANK KURTH Department of Computer Science V, University of Bonn, Bonn, Germany [email protected] We describe a generic audio codec allowing for multiple, i.e., cascaded, lossy compression without loss of perceptual quality as compared to the first generation of compressed audio. For this sake we transfer encoding information to all subsequent codecs in a cascade. The supplemental information is embedded in the decoded audio signal without causing degradations. The new method is applicable to a wide range of current audio codecs as documented by our MPEG-1 implementation. INTRODUCTION 1. AGEING EFFECTS IN CASCADED CODING Low bit rate high quality coding is used in a wide range of Input Output nowadays audio applications such as digital audio broad- Block- or Subband- Quantization Requantization Inverse casting or network conferencing. Although the decoded Transform Transform versions of the compressed data maintain very high sound quality, multiple- or tandem-coding may result in accu- Spectral Analysis/ mulated coding errors resulting from lossy data reduction Psychoacoustic Model Encoder Decoder schemes. Such multiple or tandem coding, leading to the notion of a signal’s generations, may be decribed as fol- lows. Assuming a coder operation and a corresponding Figure 1: Simplified scheme of psychoacoustic codec. ¢ decoder operation ¡ we shall call, for a given signal , ¦¥ § A general scheme of a psychoacoustic audio codec is ¢ £ ¤ ¡ ¢ ¡ the first generation and, for an integer , given in Fig. 1. In this figure we have omitted optional ¢ the £ -th generation of .
    [Show full text]