Scalable Bit Research Articles

Fast and Accurate Video PQoS Estimation over Wireless Networks

This paper proposes a curve fitting technique for fast and accurate estimation of the perceived quality of streaming media contents, delivered within a wireless network. The model accounts for the effects of various network parameters such as congestion, radio link power, and video transmission bit rate. The evaluation of the perceived quality of service (PQoS) is based on the well-known VQM objective metric, a powerful technique which is highly correlated to the more expensive and time consuming subjective metrics. Currently, PQoS is used only for offline analysis after delivery of the entire video content. Thanks to the proposed simple model, we can estimate in real time the video PQoS and we can rapidly adapt the content transmission through scalable video coding and bit rates in order to offer the best perceived quality to the end users. The designed model has been validated through many different measurements in realistic wireless environments using an ad hoc WiFi test bed.

Efficient bit-rate scalability for weighted squared error optimization in audio coding

We propose two quantization techniques for improving the bit-rate scalability of compression systems that optimize a weighted squared error (WSE) distortion metric. We show that quantization of the base-layer reconstruction error using entropy-coded scalar quantizers is suboptimal for the WSE metric. By considering the compandor representation of the quantizer, we demonstrate that asymptotic (high resolution) optimal scalability in the operational rate-distortion sense is achievable by quantizing the reconstruction error in the compandor's companded domain. We then fundamentally extend this work to the low-rate case by the use of enhancement-layer quantization which is conditional on the base-layer information. In the practically important case that the source is well modeled as a Laplacian process, we show that such conditional coding is implementable by only two distinct switchable quantizers. Conditional coding leads to substantial improvement over the companded scalable quantization scheme introduced in the first part, which itself significantly outperforms standard techniques. Simulation results are presented for synthetic memoryless Laplacian sources with mu-law companding, and for real-world audio signals in conjunction with MPEG AAC. Using the objective noise-mask ratio (NMR) metric, the proposed approaches were found to result in bit-rate savings of a factor of 2 to 3 when implemented within the scalable MPEG AAC. Moreover, the four-layer scalable coder consisting of 16-kb/s layers achieves performance close to that of the 64-kb/s nonscalable coder on the standard test database of 44.1-kHz audio

A fine granular scalable to lossless audio coder

This paper presents Advanced Audio Zip (AAZ), a fine grained scalable to lossless (SLS) audio coder that has recently been adopted as the reference model for MPEG-4 audio SLS work. AAZ integrates the functionalities of high-compression perceptual audio coding, fine granular scalable audio coding, and lossless audio coding in a single framework, and simultaneously provides backward compatibility to MPEG-4 Advanced Audio Coding (AAC). AAZ provides the fine granular bit-rate scalability from lossy to lossless coding, and such a scalability is achieved in a perceptually meaningful way, i.e., better perceptual quality at higher bit-rates. Despite its abundant functionalities, AAZ only introduces negligible overhead in terms of lossless compression performance compared with a nonscalable, lossless only audio coder. As a result, AAZ provides a universal yet efficient solution for digital audio applications such as audio archiving, network audio streaming, portable audio playing, and music downloading which were previously catered for by several different audio coding technologies, and eliminates the need for any transcoding system to facilitate sharing of digital audio contents across these application domains

Scalable audio coding for compression and loss resilient streaming

Current popular Internet audio streaming solutions impose a division between source coding (provided, for example, by MPEG layer III) and channel coding, typically accomplished in the server by means of packet re-transmission. A novel joint source and channel coder that provides packet-loss recovery and continuous bitrate scalability is presented. These functionalities are well suited to streaming audio over third and future generation wireless broadband networks.

Layered video transmission over wireless multirate DS-CDMA links

In this paper, we consider the transmission of video over wireless direct-sequence code-division multiple access (DS-CDMA) channels. A layered (scalable) video source codec is used. The layers may be time-multiplexed and transmitted over a single CDMA channel or each layer can be transmitted over a different CDMA channel. For the latter case, spreading codes of different lengths are allowed for each CDMA channel (multirate CDMA). Thus, a different number of chips per bit can be used for the transmission of each scalable layer. For a given fixed energy value per chip and chip rate, the selection of a spreading code length affects the transmitted energy per bit and bit rate for each scalable layer. An MPEG-4 source encoder is used to provide a two-layer signal-to-noise-ratio scalable bit stream. Each of the two layers is channel-coded using rate-compatible punctured convolutional codes. Then, the data are interleaved, spread, carrier-modulated, and transmitted over the wireless channel. A multipath Rayleigh fading channel model is assumed. At the other end, the signal is collected by an antenna array front. After carrier demodulation, multiple-access-interference suppressing despreading is performed using adaptive space-time auxiliary-vector (AV) filters. The choice of the AV space-time receiver is dictated by realistic channel fading rates that limit the data record available for receiver adaptation and redesign. Our experimental results demonstrate the effectiveness of such a multirate DS-CDMA system for wireless video transmission.

A framework for fully format-independent adaptation of scalable bit streams

This paper presents a framework for modeling and adaptation of arbitrary scalable multimedia bit streams in a manner that is fully format agnostic ("universal transcoding"). It is entitled structured scalable metaformats (SSM), and is based on establishing a universal model for all scalable bit streams, which in turn allows a compact specification of the set of allowed adaptations, and also how the bit stream adaptation is performed. In order to enable format-agnostic adaptation, two elements are standardized: 1) metadata associated with a scalable bit stream conveying the parameters of its SSM model, and how it is to be manipulated to obtain various adapted versions, as well as information that allows making appropriate adaptation decisions using only the model parameters and 2) a specification of outbound network and terminal constraints, for real-time decisions based on both user preferences and network conditions. By interpreting the descriptions, a universal adaptation engine can adapt the content to suit the specified needs and preferences of recipients, without knowledge of the specifics of the content, its encoding and encryption. With universal adaptation, different adaptation infrastructures are no longer needed for different types of scalable media, eliminating the high costs of traditional transcoding. Many of the SSM concepts have been adopted into the MPEG-21 Part 7 standard entitled Digital Item Adaptation.

Multi-frame GMM-based block quantisation of line spectral frequencies

In this paper, we investigate the use of the Gaussian mixture model-based block quantiser for coding line spectral frequencies that uses multiple frames and mean squared error as the quantiser selection criterion. As a viable alternative to vector quantisers, the GMM-based block quantiser encompasses both low computational and memory requirements as well as bitrate scalability. Jointly quantising multiple frames allows the exploitation of correlation across successive frames which leads to more efficient block quantisation. The efficiency gained from joint quantisation permits the use of the mean squared error distortion criterion for cluster quantiser selection, rather than the computationally expensive spectral distortion. The distortion performance gains come at the cost of an increase in computational complexity and memory. Experiments on narrowband speech from the TIMIT database demonstrate that the multi-frame GMM-based block quantiser can achieve a spectral distortion of 1 dB at 22 bits/frame, or 21 bits/frame with some added complexity.

Low-complexity GMM-based block quantisation of images using the discrete cosine transform

While block transform image coding has not been very popular lately in the presence of current state-of-the-art wavelet-based coders, the Gaussian mixture model (GMM)-based block quantiser, without the use of entropy coding, is still very competitive in the class of fixed rate transform coders. In this paper, a GMM-based block quantiser of low computational complexity is presented which is based on the discrete cosine transform (DCT). It is observed that the assumption of Gaussian mixture components in a GMM having Gauss–Markov properties is a reasonable one with the DCT approaching the optimality of the Karhunen–Loève transform (KLT) as a decorrelator. Performance gains of 6–7 dB are reported over the traditional single Gaussian block quantiser at 1 bit per pixel. The DCT possesses two advantages over the KLT: being fixed and source independent, which means it only needs to be applied once; and the availability of fast and efficient implementations. These advantages, together with bitrate scalability, result in a block quantiser that is considerably faster and less complex while the novelty of using a GMM to model the source probability density function is still preserved.

A scalable video codec for layered video streaming

A wavelet-based video codec that performs layering of encoded bit streams to support layered scalable video streaming is presented in this paper. The proposed codec can generate separate bit streams comprising different cumulative layers. A new sorting algorithm is incorporated to produce spatial and temporal resolution scalable bit streams, and a specially designed layer-ID is used to identify the layer that a particular data packet belongs to. The proposed codec is more suitable for real network environments as it is able to correctly decode bit streams that have experienced losses incurred during network transmission loss.

Stochastic Modeling of the Spatiotemporal Wavelet Coefficients and Applications to Quality Enhancement and Error Concealment

We extend a stochastic model of hierarchical dependencies between wavelet coefficients of still images to the spatiotemporal decomposition of video sequences, obtained by a motion-compensated 2D+t wavelet decomposition. We propose new estimators for the parameters of this model which provide better statistical performances. Based on this model, we deduce an optimal predictor of missing samples in the spatiotemporal wavelet domain and use it in two applications: quality enhancement and error concealment of scalable video transmitted over packet networks. Simulation results show significant quality improvement achieved by this technique with different packetization strategies for a scalable video bit stream.

SMART: An Efficient, Scalable, and Robust Streaming Video System

SMART, the acronym of scalable media adaptation and robust transport, is a suite of compression and transmission technologies for efficient, scalable, adaptive, and robust video streaming over the best-effort Internet. It consists of two indispensable parts: SMART video coding and SMART video streaming. The SMART video coding part is an efficient DCT-based universal fine granularity scalable coding scheme. Since the SMART video coding scheme adopts multiple-loop prediction and drifting reduction techniques at the macroblock level, it can achieve high coding efficiency at a wide range of bit rates. More importantly, it provides all sorts of scalabilities, that is, quality, temporal, spatial, and complexity scalabilities, in order to accommodate heterogeneous time-variant networks and different devices. The SMART video streaming part is a transport scheme that fully takes advantages of the special features of the scalable bit streams. An accurate bandwidth estimation method is first discussed as the prerequisite of network adaptation. Then, flexible error resilience technique and unequal error protection strategy are investigated to enhance the robustness of streaming SMART bit stream. The SMART system shows excellent performances with regard to high coding efficiency, flexible channel bandwidth adaptation, smooth playback, and superior error robustness in static and dynamic experiments.

Adaptive motion-compensation fine-granular-scalability (AMC-FGS) for wireless video

Transmission of video over wireless and mobile networks requires a scalable solution that is capable of adapting to the varying channel conditions in real-time (bit-rate scalability). Furthermore, video content needs to be coded in a scalable fashion to match the capabilities of a variety of devices (complexity scalability). These two scalability properties provide the flexibility that is necessary to satisfy the "anywhere, anytime and anyone" network paradigm of wireless systems. MPEG-4 fine-granular-scalability (FGS) is a flexible low-complexity solution for video streaming over heterogeneous networks (e.g., the Internet and wireless networks) and is highly resilient to packet losses. However, the flexibility and packet-loss resilience come at the expense of decreased coding efficiency compared with nonscalable coding. A novel scalable video-coding framework and corresponding compression methods for wireless video streaming is introduced. Building on the FGS approach, the proposed framework, which we refer to as adaptive motion-compensation FGS (AMC-FGS), provides improved video quality of up to 2 dB. Furthermore, the new scalability structures provide the FGS framework with the flexibility to provide tradeoffs between resilience, higher coding efficiency and terminal complexity for more efficient wireless transmission.

A framework for efficient progressive fine granularity scalable video coding

A basic framework for efficient scalable video coding, namely progressive fine granularity scalable (PFGS) video coding is proposed. Similar to the fine granularity scalable (PGS) video coding in MPEG-4, the PFGS framework has all the features of FGS, such as fine granularity bit-rate scalability, channel adaptation, and error recovery. On the other hand, different from the PGS coding, the PFGS framework uses multiple layers of references with increasing quality to make motion prediction more accurate for improved video-coding efficiency. However, using multiple layers of references with different quality also introduces several issues. First, extra frame buffers are needed for storing the multiple reconstructed reference layers. This would increase the memory cost and computational complexity of the PFGS scheme. Based on the basic framework, a simplified and efficient PFGS framework is further proposed. The simplified PPGS framework needs only one extra frame buffer with almost the same coding efficiency as in the original framework. Second, there might be undesirable increase and fluctuation of the coefficients to be coded when switching from a low-quality reference to a high-quality one, which could partially offset the advantage of using a high-quality reference. A further improved PFGS scheme can eliminate the fluctuation of enhancement-layer coefficients when switching references by always using only one high-quality prediction reference for all enhancement layers. Experimental results show that the PFGS framework can improve the coding efficiency up to more than 1 dB over the FGS scheme in terms of average PSNR, yet still keeps all the original properties, such as fine granularity, bandwidth adaptation, and error recovery. A simple simulation of transmitting the PFGS video over a wireless channel further confirms the error robustness of the PFGS scheme, although the advantages of PFGS have not been fully exploited.

Method device and system for an efficient noise injection process for low bitrate audio compression

The present invention provides a device, method and system of noise injection to maximize compressed audio quality while enabling bitrate scalability. It includes at least one of an encoder and a decoder. The encoder includes a zero detection unit, coupled to receive a frequency domain quantized signal, for determining a control signal that indicates whether noise injection is implemented and a normalization computation unit, coupled to receive at least unquantized signal values and the control signal, for determining a normalization term in accordance with the control signal. The decoder includes a zero detection unit, coupled to receive a frequency domain quantized signal, for determining a control signal that indicates when noise injection is active and a noise generation and normalization unit, coupled to receive a normalization term and the control signal, for generating, normalizing, and injecting a predetermined noise signal where indicated by the control signal.