Quality of experience ties together user perception, experience, and expectations to application and network performance, typically expressed by quality of service parameters. Quantitative relationships between QoE and QoS are required in order to be able to build effective QoE control mechanisms onto measurable QoS parameters. Against this background, this article proposes a generic formula in which QoE and QoS parameters are connected through an exponential relationship, called IQX hypothesis. The formula relates changes of QoE with respect to QoS to the current level of QoE, is simple to match, and its limit behaviors are straightforward to interpret. It validates the IQX hypothesis for streaming services, where QoE in terms of Mean Opinion Scores is expressed as functions of loss and reordering ratio, the latter of which is caused by jitter. For web surfing as the second application area, matchings provided by the IQX hypothesis are shown to outperform previously published logarithmic functions. We conclude that the IQX hypothesis is a strong candidate to be taken into account when deriving relationships between QoE and QoS parameters.

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A generic quantitative relationship between quality of experience and quality of service

The present disclosure pertains to an image-processing device and method that make it possible to suppress block noise. A βLUT_input calculator and a clip processor determine βLUT_input, which is a value inputted to an existing-β generator and an expanded-β generator. When the value of βLUT_input qp from the clip processor is 51 or less, the existing-β generator determines β using a LUT conforming to the HVEC standard, and supplies same to a filter process determination unit. When the value of βLUT_input qp from the clip processor is larger than 51, the expanded-β generator determines the expanded β and supplies same to the filter process determination part. This disclosure, for example, can be applied to an image processing device.

Image processing device and method

VoIP: A comprehensive survey on a promising technology

Voice over IP (VoIP) has become a popular protocol for making phone calls over the Internet. Due to the potential transit of sensitive conversations over untrusted network infrastructure, it is well understood that the contents of a VoIP session should be encrypted. However, we demonstrate that current cryptographic techniques do not provide adequate protection when the underlying audio is encoded using bandwidth-saving Variable Bit Rate (VBR) coders. Explicitly, we use the length of encrypted VoIP packets to tackle the challenging task of identifying the language of the conversation. Our empirical analysis of 2,066 native speakers of 21 different languages shows that a substantial amount of information can be discerned from encrypted VoIP traffic. For instance, our 21-way classifier achieves 66% accuracy, almost a 14-fold improvement over random guessing. For 14 of the 21 languages, the accuracy is greater than 90%. We achieve an overall binary classification (e.g., "Is this a Spanish or English conversation?") rate of 86.6%. Our analysis highlights what we believe to be interesting new privacy issues in VoIP.

/pdf/language-identification-of-encrypted-voip-traffic-alejandra-32n851prr0.pdf

Language identification of encrypted VoIP traffic: Alejandra y Roberto or Alice and Bob?

A signal processing system which discriminates between voice signals and data signals modulated by a voiceband carrier. The signal processing system includes a voice exchange, a data exchange and a call discriminator. The voice exchange is capable of exchanging voice signals between a switched circuit network and a packet based network. The signal processing system also includes a data exchange capable of exchanging data signals modulated by a voiceband carrier on the switched circuit network with unmodulated data signal packets on the packet based network. The data exchange is performed by demodulating data signals from the switched circuit network for transmission on the packet based network, and modulating data signal packets from the packet based network for transmission on the switched circuit network. The call discriminator is used to selectively enable the voice exchange and data exchange.

Voice and Data Exchange Over a Packet Based Network

This document specifies a speech codec suitable for robust voice
communication over IP. The codec is developed by Global IP Sound
(GIPS). It is designed for narrow band speech and results in a payload
bit rate of 13.33 kbit/s for 30 ms frames and 15.20 kbit/s for 20 ms
frames. The codec enables graceful speech quality degradation in the
case of lost frames, which occurs in connection with lost or delayed
IP packets. This memo defines an Experimental Protocol for the
Internet community.

Internet Low Bit Rate Codec (iLBC)

In this paper, we discuss the internet low bit rate codec (iLBC) with an emphasis on the frame-independent long-term prediction. The frame-independent long-term prediction is a method to exploit pitch-lag correlations in the encoding of speech without suffering multiple-frame speech degradation in connection with transmission loss. We present mean opinion scores for the iLBC codec and show by means of signal examples how the nature of degradation in a predictive codec based on frame-independent long-term prediction differs from that of traditional CELP codecs.

iLBC - a linear predictive coder with robustness to packet losses

Sparseness is reduced in an input digital signal (A) which includes a first sequence of sample values. An output digital signal (B) is produced in response to the input digital signal. The output digital signal includes a second sequence of sample values, which second sequence of sample values has a greater density of non-zero sample values than the first sequence of sample values.

Reducing sparseness in coded speech signals

A post-processing method for a speech decoder (1) which gives a decoded speech signal in the time domain in order to obtain high frequency resolution from a frequency spectrum having non-harmonic and noise deficiencies. The method comprises the following steps: a) transforming (21) the decoded time domain signal to a frequency domain signal by means of a high frequency resolution transform (FFT); b) analysing (5) the energy distribution of said frequency domain signal throughout its frequency area (4 kHz) to find the disturbing frequency components and to prioritize such frequency components which are situated in the higher part of the frequency spectrum; c) finding (6) the suppression degree for said disturbing frequency components based on said prioritizing; d) controlling a post-filtering (31) of said transform in dependence of said finding (6); and e) inverse transforming (4) the post-filtered transform in order to obtain a post-filtered decoded speech signal in the time domain.

A high resolution post processing method for a speech decoder

Adaptive speech coding includes receiving an original speech signal, performing on the original speech signal a current coding operation, and adapting the current coding operation (11) in response to information used in the current coding operation (17, 18, 19). Adaptive speech decoding includes receiving coded information, performing a current decoding operation (200) on the coded information, and adapting the current decoding operation in response to information used in the current decoding operation (17, 18, 19).

Roar Hagen

Papers

Internet Low Bit Rate Codec (iLBC)

iLBC - a linear predictive coder with robustness to packet losses

Reducing sparseness in coded speech signals

A high resolution post processing method for a speech decoder

Speech coding including soft adaptability feature