Graceful Quality Degradation Research Articles

Approximate Multipliers With Dynamic Truncation for Energy Reduction via Graceful Quality Degradation

In approximate operators, dynamic truncation allows trading off energy and quality of computation at runtime. Although it exploits the specificity of the data being processed, its significant energy overhead over simple static truncation fundamentally limits its energy benefits. This brief describes a simple and efficient design methodology that reduces the energy consumption of dynamically truncated multipliers, based on a smart mapping of the partial products. A configurable hardware correction strategy is also proposed to enable graceful quality degradation, as well as more aggressive energy reduction at a given quality. When applied to Wallace multipliers, the proposed approach achieves quality, in terms of Mean Error Distance, up to 11× higher than the conventional dynamic truncation, at the same energy. In the case study of Discrete Cosine Transform compression, the proposed approximate multiplier reaches image qualities by 15-35% better, compared to prior art.

Energy-Quality Scalable Memory-Frugal Feature Extraction for Always-On Deep Sub-mW Distributed Vision

In this work, an energy-quality (EQ) scalable and memory-frugal architecture for video feature extraction is introduced to reduce circuit complexity, power and silicon area. Leveraging on the inherent resiliency of vision against noise and inaccuracies, the proposed approach introduces properly selected EQ tuning knobs to reduce the energy of feature extraction at graceful quality degradation. As opposed to prior art, the proposed architecture enables the adjustment of such knobs, and adapts its cycle-level timing to reduce the amount of computation per frame at lower quality targets. As further benefit, the approach adds opportunities for energy reduction via aggressive voltage scaling. The proposed architecture mitigates the traditionally dominant area/energy of the on-chip memory by reducing the number of pixels stored on chip, introducing memory access reuse and on-the-fly computation. At the same time, EQ tuning preserves the ability to conventionally operate at maximum quality, when required by the task or the visual context. A 0.55 mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> testchip in 40nm exhibits power down to 82μW at 5fps frame rate (i.e., 33X lower than prior art), while assuring successful object detection at VGA resolution. To the best of the authors' knowledge, this is the first feature extractor with sub-mW operation and sub-mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> area, making the proposed approach well suited for tightly power-constrained and low-cost distributed vision systems (e.g., video sensor nodes).

A metadata-free pure soft broadcast scheme for image and video transmission

The SoftCast, as an analog-like transmission scheme, has been proposed to provide graceful video quality degradation for wireless communication scenarios where channel condition usually varies dynamically and unpredictably. It attempts to eliminate the cliff effect that conventional communication systems usually suffer from. The unequal power allocation minimizes the total distortion by optimally scaling the transform coefficients according to their statistics. However, the sender has to transmit considerable amount of metadata over a digital channel with strong channel codes to assist in decoding at the receiver. And the transmission will still break down completely if the metadata are corrupted. Hence, SoftCast can just alleviate the cliff effect. To eliminate the cliff effect while maintaining the image and video quality, a feasible metadata-free pure soft broadcast scheme for image and video transmission is proposed in this paper. Specifically, we apply an effective chunk-based blind estimation method to reconstruct the original signal from the received noisy signal without using power scaling factors. Thus, our scheme can eliminate the cliff effect. In addition, since smaller chunks can be used, better performance than SoftCast has been achieved. Experimental results show that our scheme outperforms SoftCast by about 4dB and provides graceful quality degradation for a wide channel signal to noise ratio range.

Energy-Quality Scalable Adders Based on Nonzeroing Bit Truncation

Approximate addition is a technique to trade off energy consumption and output quality in error-tolerant applications. In prior art, bit truncation has been explored as a lever to dynamically trade off energy and quality. In this brief, an innovative bit truncation strategy is proposed to achieve more graceful quality degradation compared to state-of-the-art truncation schemes. This translates into energy reduction at a given quality target. When applied to a ripple-carry adder, the proposed bit truncation approach improves quality by up to 8.5 dB in terms of peak signal-to-noise ratio, compared to traditional bit truncation. As a case study, the proposed approach was applied to a discrete cosine transform engine. In comparison with prior art, the proposed approach reduces energy by 20%, at insignificant delay and silicon area overhead.

OmniCast: Wireless Pseudo-Analog Transmission for Omnidirectional Video

Wireless virtual reality (VR) applications that provide users extraordinary viewing experience are now drawing great attentions. Transmitting VR video via wireless channel to users’ head-mounted display devices efficiently with low latency is very important for many emerging VR applications. In this paper, we propose a pseudo-analog transmission framework named OmniCast, which provides graceful quality degradation and competitive performance for unpredictably varying wireless channels while featuring low latency, low complexity, and low energy cost. In particular, we analyze the influence of projection between the spherical representation and the 2-D plane representation, and derive a power optimization scheme to minimize the distortion on the sphere. In addition, we develop an approach to measure the efficiency of decorrelation transform in the spherical domain. Based on that, an appropriate transform option can be determined. Experimental results show that the proposed framework improves the transmission efficiency of omnidirectional videos, while achieving elegant quality degradation for channel fluctuation in a wide channel SNR range.

Distributed Compressive Sensing for Cloud-Based Wireless Image Transmission

We consider efficient image transmission via time-varying channels. To improve the performance, we propose a new distributed compressive sensing (CS) scheme that can leverage similar images in the cloud. It is featured by channel SNR and bandwidth scalability, high efficiency, and low encoding complexity. For each image, a compressed thumbnail is first transmitted after forward error correction (FEC) and modulation to retrieve similar images and generate a side information (SI) in the cloud. The residual image after subtracting the decompressed thumbnail is then coded and transmitted by CS through a very dense constellation without FEC. The linearly and ratelessly generated CS measurements make it capable of achieving both graceful quality degradation (GD) with the channel SNR and bandwidth scalability in a universal scheme. A mode decision and transform-domain power allocation are introduced for better bandwidth usage and protection against channel errors. At the decoder, a two-step CS decoding is performed to recover the residual signal, where both the local and nonlocal correlations within the image and that with the SI are exploited. Simulations on landmark images and an AWGN channel show that the received image quality gracefully increases with the channel SNR and bandwidth. Furthermore, it outperforms existing schemes both subjectively and objectively by up to 11 dB gains compared with the state-of-the-art transmission scheme with GD, i.e. SoftCast.

Progressive Pseudo-analog Transmission for Mobile Video Streaming

We propose a progressive pseudo-analog video transmission scheme that simultaneously handles SNR and bandwidth variations with graceful quality degradation for mobile video streaming. With the inherited SNR-adaptability from pseudo-analog transmission, the proposed progressive solution acquires bandwidth adaptability through an innovative scheduling algorithm with optimal power allocation. The basic idea is to aggressively transmit or retransmit important coefficients so that distortion is minimized at the receiver after each received packet. We derive the closed-form expression of reduced distortion for each packet under given transmission power and known channel conditions, and show that the optimal solution can be obtained with a water-filling algorithm. We also illustrate through analyses and simulations that a near-optimal solution can be found through approximation when only statistical channel information is available. Simulations show that our solution approaches the performance upper bound of pseudo-analog transmission in an additive white Gaussian noise channel and significantly outperforms existing pseudo-analog solutions in a fast Rayleigh fading channel. Trace-driven emulations are also carried out to demonstrate the advantage of the proposed solution over the state-of-the-art digital and pseudo-analog solutions under a real dramatically varying wireless environment.

Sparse $\ell _{1}$-Optimal Multiloudspeaker Panning and Its Relation to Vector Base Amplitude Panning

Panning techniques, such as vector base amplitude panning (VBAP), are a widely used practical approach for spatial sound reproduction using multiple loudspeakers. Although limited to a relatively small listening area, they are very efficient and offer good localization accuracy, timbral quality, as well as a graceful degradation of quality outside the sweet spot. The aim of this paper is to investigate optimal sound reproduction techniques that adopt some of the advantageous properties of VBAP, such as the sparsity and the locality of the active loudspeakers for the reproduction of a single audio object. To this end, we state the task of multiloudspeaker panning as an ${\ell _{1}}$ optimization problem. We demonstrate and prove that the resulting solutions are exactly sparse. Moreover, we show the effect of adding a nonnegativity constraint on the loudspeaker gains in order to preserve the locality of the panning solution. Adding this constraint, ${\ell _{1}}$ -optimal panning can be formulated as a linear program. Using this representation, we prove that unique ${\ell _{1}}$ -optimal panning solutions incorporating a nonnegativity constraint are identical to VBAP using a Delaunay triangulation for the loudspeaker setup. Using results from linear programming and duality theory, we describe properties and special cases, such as solution ambiguity, of the VBAP solution.

Error-Resilient Video Encoding Using Parallel Independent Signature Processing

Soft errors resulting from encoding video sequences on unreliable hardware can create significant artifacts in decoded video sequences, contributing to extreme video quality degradation. Modern systems are required to operate under increasingly challenging constraints, including smaller feature sizes and lower operating voltage, increasing the likelihood of soft errors in the video encoding hardware. These conditions are of particular concern for energy-limited battery-operated systems since they may be required to operate in nonideal environments and/or continue operating with a practically depleted energy source. The proposed parallel independent signature processing design performs error detection and mitigation in video encoding hardware, enabling a graceful degradation of quality when encoding by using unreliable hardware. The effects of soft errors are minimized by preventing the error propagation normally associated with errors in encoded video sequences. This allows for the recovery of quality when errors are present in the video encoding system. Conventional video encoding techniques are designed to handle worst case error rates by increasing gate sizes and/or increasing the operating voltage of the system. Such designs have error-rate limits, and when these limits are reached, the systems tend to fail catastrophically, resulting in an unrecoverable signal. The proposed design allows for single upset events to translate into single transient artifacts in a decoded video sequence.

QoE-driven multi-user scheduling and rate adaptation with reduced cross-layer signaling for scalable video streaming over LTE wireless systems

The scarcity of the available radio spectrum coupled with the growing popularity of bandwidth intensive mobile video applications poses a huge challenge to network operators. The solution of over-provisioning the network is not economical; hence, an appropriate strategy for scheduling and resource allocation among the users in the system is of crucial importance. This work focuses on scheduling multiple video flows on the downlink of a wireless system based on orthogonal frequency division multiple access (OFDMA), such as Long-Term Evolution (LTE) and LTE-A (LTE-Advanced) standards. We propose a joint multi-user scheduling and multi-user rate adaptation strategy providing an appropriate trade-off between efficiency and fairness, while ensuring high quality of experience (QoE) for the end users. We consider Scalable Video Coding (SVC) which facilitates the truncation of bit streams, thus allowing graceful degradation of video quality in the event of wireless channel variations or network congestion. The proposed scheduler utilizes QoE-aware priority marking, where video layers are mapped to priority classes and targets at minimizing delay bound violations for the most important priority classes under congestion. In order to reduce congestion, we propose multi-user rate adaptation at the MAC layer via a novel dynamic filtering policy for QoE-based priority classes. Simulation results show that the proposed approach delivers to the end users a similar QoE as delivered by the state-of-the-art cross-layer approaches, where extensive cross-layer signaling, additional video rate adaptation modules at the core network, and frequent link probing from the wireless access network to the rate adaptation modules are required. The latter approaches are not implemented in real systems due to the aforementioned drawbacks, while our approach can be implemented without major modifications in the standard behavior of existing networks and equipment. The proposed framework can deliver delay-sensitive traffic as well as delay-tolerant best-effort traffic.

A quality-aware Energy-scalable Gaussian Smoothing Filter for image processing applications

Energy-efficient design is the prime requirement for modern portable devices as these devices employ compute intensive image/video processing cores which produces output for human consumption. The limited perception of human sense can be exploited to improve energy-efficacy via approximate designs. In this paper, a novel quality-aware Energy-Scalable Gaussian Smoothing Filter (ES-GSF) is proposed that significantly reduces energy requirement at the cost of slightly reduced quality. The energy scalability within ES-GSF is achieved by exploiting the relative significance of kernel coefficients existing on different boundaries. The ES-GSF computes significant boundaries for the given energy budget. Simulation results show that ES-GSF consumes 30.46% reduced energy with graceful quality degradation over the well-known existing architectures. Further, the ES-GSF can scale energy up to 65.05% when switched from high quality mode to energy-efficient mode. The efficacy of the proposed filter is demonstrated in edge detection where ES-GSF embedded edge detectors consume 29.9% less energy over the well-known existing architectures.

QoS-aware scalable video streaming using data distribution service

Enabling Real-Time video streaming over wireless networks is a challenging task due to the time-varying channel conditions and the limited network resources. The instability of wireless networks leads to problems such as limited and time-varying bandwidth, and unexpected traffic congestion when transmitting a burst of video streams. In Real-Time video streaming, each frame must be delivered and decoded by its playback time. Recently, layer coding (LC) has enabled Real-Time and scalable video streaming to clients of heterogeneous capabilities by dropping upper enhancement layers without the need of re-encoding and with much less bit rate. However, layer coding still facing unfair layer protection problem in which packets from the base or lower layers might be dropped while there is a chance to drop packets from the upper enhancement layers. Losing packets from the base layer can significantly affect the delivered video quality and sometimes lead to an interruption especially in error-prone networks as wireless networks. Architectural solutions at the middleware level introduce higher flexibility, more efficiency in development time and more QoS control. This work investigates the behavior of video streaming over Real-Time publish-subscribe based middleware. We propose and develop an unequal layer protection mechanism for Real-Time video streaming based on the Data Distribution Service (DDS) middleware. A combination of video quality of service (QoS) is proposed to adapt the video transmission to the time-varying network changes. The performance of Real-Time video streaming is measured over IEEE 802.11 g WLAN network. The results show a graceful degradation of video quality while maintaining a robust video streaming free of visible error or interruptions. This is due to the intentionally dropping of some enhancement video stream layers in order to protect lower layers, and therefore maintain a continuous video flow of acceptable quality. Numerical results indicate that the proposed scheme is able to achieve higher throughput and improve the average received video quality especially in a large number of video subscribers.

A programming model and runtime system for significance-aware energy-efficient computing

We introduce a task-based programming model and runtime system that exploit the observation that not all parts of a program are equally significant for the accuracy of the end-result, in order to trade off the quality of program outputs for increased energy-efficiency. This is done in a structured and flexible way, allowing for easy exploitation of different points in the quality/energy space, without adversely affecting application performance. The runtime system can apply a number of different policies to decide whether it will execute less-significant tasks accurately or approximately. The experimental evaluation indicates that our system can achieve an energy reduction of up to 83% compared with a fully accurate execution and up to 35% compared with an approximate version employing loop perforation. At the same time, our approach always results in graceful quality degradation.

Distributed Scheduling for Low-Delay and Loss-Resilient Media Streaming With Network Coding

Network coding (NC) has been shown to be very effective for collaborative media streaming applications. A pivotal issue in media streaming with NC lies in the packet scheduling policy at the network nodes, which affects the perceived media quality. In this paper, we address the problem of finding the packet scheduling policy that maximizes the number of media segments recovered in the network. We cast this as a distributed minimization problem and propose heuristic solutions that make the proposed framework robust to infrequent or inaccurate feedback information. Moreover, the proposed framework accounts for the properties of layered and multiple description encoded media to provide graceful quality degradation in case of packet losses or lack of upload bandwidth. Experimental results on a local testbed as well as PlanetLab suggest that our scheduling framework achieves better media quality, lower playback delay, and lower bandwidth consumption than a random-push scheme.

AMFS

We propose a powerful hardware architecture for pixel shading, which enables flexible control of shading rates and automatic shading reuse between triangles in tessellated primitives. The main goal is efficient pixel shading for moderately to finely tessellated geometry, which is not handled well by current GPUs. Our method effectively decouples the cost of pixel shading from the geometric complexity. It thereby enables a wider use of tessellation and fine geometry, even at very limited power budgets. The core idea is to shade over small local grids in parametric patch space, and reuse shading for nearby samples. We also support the decomposition of shaders into multiple parts, which are shaded at different frequencies. Shading rates can be locally and adaptively controlled, in order to direct the computations to visually important areas and to provide performance scaling with a graceful degradation of quality. Another important benefit of shading in patch space is that it allows efficient rendering of distribution effects, which further closes the gap between real-time and offline rendering.

FPGA Architecture for Kriging Image Interpolation

This paper proposes an ultrafast scalable embedded image compression scheme based on discrete cosine transform. It is designed for general network architecture that guarantees maximum end-to-end delay (EED), in particular the Distributed Multimedia Plays (DMP) architecture. DMP is designed to enable people to perform delay-sensitive real-time collaboration from remote places via their own collaboration space (CS). It requires much lower EED to achieve good synchronization than that in existing teleconference systems. A DMP node can drop packets from networked CSs intelligently to guarantee its local delay and degrade visual quality gracefully. The transmitter classifies visual information in an input image into priority ranks. Included in the bitstream as side information, the ranks enable intelligent packet dropping. The receiver reconstructs the image from the remaining packets. Four priority ranks for dropping are provided. Our promising results reveal that, with the proposed compression technique, maximum EED can be guaranteed with graceful degradation of image quality. The given parallel designs for its hardware implementation in FPGA shows its technical feasibility as a module in the DMP architecture.

Improved multiple description wavelet based image coding using subband uniform quantization

The objective of multiple description coding (MDC) is to encode a source into multiple descriptions supporting different quality levels of reconstruction. In this paper, we use the multiple description transform coding (MDTC) algorithm based on the wavelet transform that has been shown to be robust to packet losses allowing a graceful quality degradation. The case of transmitting still images with four descriptions is considered. We propose to use subband uniform quantization with different quantization steps, optimized using a genetic algorithm (GA), when compressing to a target bit-rate. Simulation results show that the proposed method offers substantial improvements in the case of packet loss when compared to previously reported work that applies uniform quantization with a fixed step size.

Ultra-Low-Power and Robust Digital-Signal-Processing Hardware for Implantable Neural Interface Microsystems

Implantable microsystems for monitoring or manipulating brain activity typically require on-chip real-time processing of multichannel neural data using ultra low-power, miniaturized electronics. In this paper, we propose an integrated-circuit/architecture-level hardware design framework for neural signal processing that exploits the nature of the signal-processing algorithm. First, we consider different power reduction techniques and compare the energy efficiency between the ultra-low frequency subthreshold and conventional superthreshold design. We show that the superthreshold design operating at a much higher frequency can achieve comparable energy dissipation by taking advantage of extensive power gating. It also provides significantly higher robustness of operation and yield under large process variations. Next, we propose an architecture level preferential design approach for further energy reduction by isolating the critical computation blocks (with respect to the quality of the output signal) and assigning them higher delay margins compared to the noncritical ones. Possible delay failures under parameter variations are confined to the noncritical components, allowing graceful degradation in quality under voltage scaling. Simulation results using prerecorded neural data from the sea-slug (Aplysia californica) show that the application of the proposed design approach can lead to significant improvement in total energy, without compromising the output signal quality under process variations, compared to conventional design approaches.

Multiple Description Distribution Preserving Quantization

The notion of a multiple description quantizer (MDQ) includes providing multiple distortion levels. If a human observer is involved, the design of MDQ requires a suitable distortion measure to achieve a graceful quality degradation in the case of description losses. While the mean squared error is a ubiquitous distortion measure for many classic MDQ schemes, it is known to be perceptually relevant only at low distortions. We propose a new MDQ designed according to an unconventional distortion criterion that combines the mean squared error with a constraint on the probability distribution of the reconstructed signal. The performance of the new MDQ is shown to approach that of the classic MDQ asymptotically as rate increases. However, once applied in the context of transform audio coding, the new MDQ significantly outperforms a classic MDQ in perceptual tests. The new scheme is suitable for a wide range of distortions and renders a seamless transition between coding that preserves signal features and coding of a waveform.

Error Resilient IPTV for an IEEE 802.16e Channel

Data-partitioning of IPTV video streams is a way of providing graceful quality degradation in a form that will work in good and difficult wireless channel conditions, as experienced by mobile devices. This paper’s proposal is to combine redundant slice protection along with an adaptive channel coding scheme that is also proposed in the paper. Adaptive channel coding is achieved by retransmission when necessary of additional redundant data to reconstruct corrupted packets. In the proposal, outright packet loss is provided for by a form of redundant slice protection. The paper finds that it is preferable: not to simply protect only the highest priority packets; that a moderate quantization level should be employed; and that video quality is differentiated by content type. It is important also to configure the partitioning correctly to remove inter-partition dependencies when possible.