303 research outputs found
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Error correction by means of arithmetic codes: an application to resilient image transmission
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Joint source/channel coding and MAP decoding of arithmetic codes
No full textIn this paper, a novel maximum a posteriori (MAP) estimation approach is employed for error correction of arithmetic codes with a forbidden symbol. The system is founded on the principle of joint source channel coding, which allows one to unify the arithmetic decoding and error correction tasks into a single process, with superior performance compared to traditional separated techniques. The proposed system improves the performance in terms of error correction with respect to a separated source and channel coding approach based on convolutional codes, with the additional great advantage of allowing complete flexibility in adjusting the coding rate. The proposed MAP decoder is tested in the case of image transmission across the additive white Gaussian noise channel and compared against standard forward error correction techniques in terms of performance and complexity. Both hard and soft decoding are taken into account, and excellent results in terms of packet error rate and decoded image quality are obtained
Optimization of Scalable Broadcast for a Large Number of Antennas
No full textIn this paper, for a system incorporating a large number of antennas, we address the optimal space-time coding of multimedia scalable sources, which require unequal target error rates in their bitstream. First, in terms of the number of antennas, we analyze the behavior of the crossover point of the outage probability curves for the vertical Bell Laboratories space-time (V-BLAST) architecture with a linear or a maximum-likelihood receiver, and orthogonal space-time block codes (OSTBCs). We prove that, as the number of antennas increases with the transmission data rate fixed, the crossover point in outage probability monotonically decreases. This holds for any data rate employed by the system and is valid over propagation channels such as spatially correlated Rayleigh or Rician fading channels, as well as independent and identically distributed Rayleigh channels. We next show that, over such propagation channels with a large number of antennas, those analytical results can be used to simplify the computational complexity involved with the optimal space-time coding of a sequence of scalable packets, with no performance degradation. © 2016 IEEE.FALS
Optimization of Multimedia Progressive Transmission Over MIMO Channels
No full textThis paper studies the optimal transmission of multimedia progressive sources, which require unequal target error rates in their bitstream, over multiple-input-multiple-output (MIMO) channels. First, we derive the information outage probability expression of a space-time code for an arbitrarily given piecewise-linear diversity-multiplexing tradeoff (DMT) function and the conditions for the existence of a crossover point of the information outage probability curves of the space-time codes. We prove that as long as the crossover point of the outage probabilities exists, as spectral efficiency increases, the crossover point in the signal-to-noise ratio (SNR) monotonically increases, whereas that of the outage probability monotonically decreases. This analysis can be applied to any space-time code, receiver, and propagation channel with a given DMT function. As a specific example, we analyze the two-layer diagonal Bell Labs space-time architecture (D-BLAST) with a group zero-forcing receiver, the vertical BLAST (V-BLAST) with a minimum mean-square error receiver, and orthogonal space-time block codes (OSTBCs), and prove the monotonic behavior of the crossover point for those codes. Based on that, with respect to D-BLAST, V-BLAST, and OSTBC, we derive a method for the optimal space-time coding of a sequence that contains numerous progressive packets. We show that by employing the optimization method rather than exhaustive search, the computational complexity involved with optimal space-time coding can be exponentially reduced without losing any peak SNR performance. © 2015 IEEE
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Restoration and Enhancement of Images Degraded by Light Scattering and Absorption
No full textImages degraded by light scattering and absorption such as hazy, sandstorm, and underwater images often suffer from color distortion and low contrast because of light traveling through turbid media. This can prevent systems that operate outdoors in different lighting conditions from functioning properly, for example, video surveillance systems, autopilot systems and intelligent transportation systems, which include automatic license plate recognition, automatic traffic counting, etc. Therefore, it is desirable to develop an effective method to restore color and enhance contrast for these images. This thesis presents novel work to advance research on image restoration and enhancement for such images.To enhance or restore such a degraded image, the image formation model is often used to describe it as a ``clear" image blended with an ambient light based on the scene transmission computed using the scene depth from the camera. The transmission describes the portion of the scene radiance which is not scattered or absorbed and which reaches the camera. By reversing the image formation process, one can attain the scene radiance from a degraded image, which is a ``clear" image. However, it involves solving an ill-posed and under-constrained problem because we need to estimate both the ambient light and scene transmission from a single degraded image.To attack this problem, we proposed to use image blurriness to estimate ambient light and scene depth for underwater images. Furthermore, we extended it by combining light absorption and blurriness to estimate scene depth for underwater scenes in different lighting conditions and color tones. For any images degraded by light scattering and absorption, not limited to underwater ones, we proposed a generalization of the common dark channel prior approach for ambient light and transmission estimation. Additionally, adaptive color correction is incorporated into the image formation model for removing color casts while restoring contrast. Based on the experimental results, our proposed algorithms outperform, both subjectively and objectively, other state-of-the-art algorithms based on the image formation model
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Quality Evaluation, Denoising and Inpainting for Point Clouds Compressed by V-PCC
No full textAugmented reality (AR), virtual reality (VR) and immersive video, as emerging types of multimedia, have recently gained more and more attention. With the development of devices that can capture 3D content and a rapid increase of related applications, the delivery and storage of 3D content have become an important research area. MPEG hosted a Call for Proposals to collect ideas to efficiently compress 3D content in three categories: static point clouds, dynamic point cloud sequences and dynamic acquisition. Among the proposals, Video-based Point Cloud Compression (V-PCC) achieves the highest quality for the second category, dynamic point cloud sequences, under a bit rate constraint. However, the V-PCC framework is not spatially scalable. In this research, interpolation components are proposed for the V-PCC framework to make it suitable for flexible spatial resolution. As outliers might be brought in by the interpolation, a patch-aware averaging filter is applied to eliminate most outliers. Experimental results show that the interpolation component performs well both on objective evaluation and subjective visual quality. Point cloud and mesh are the two main representations for 3D content. While compression methods for them are actively studied, there are few studies of their perceptual compression quality and none that consider observation distance. We studied the perceptual quality of compressed 3D sequences, for both a point cloud compression method (V-PCC) and a mesh-based compression method (Triangle FAN (TFAN)). Two main factors that could impact perceptual quality are considered, bit rate and observation distance. Evaluation of perceptual quality is carried out both by collecting viewer opinion scores of the compressed sequences separately, and with a side-by-side comparison. A functional model for mesh and point cloud compression quality is estimated to predict Mean Opinion Score (MOS) which yields high Pearson correlation and rank correlation scores with measured MOS. Although V-PCC achieves the highest quality among methods proposed to MPEG, outliers and various other artifacts can degrade the V-PCC quality especially when high quantization parameter (QP) is set. After examining the causes and types of V-PCC artifacts that occur, we propose a framework to remove the highly noticeable outlier and crack artifacts caused by V-PCC so as to improve compressed point cloud visual quality. A subjective experiment showed that our approach significantly improves visual quality, and the improvement becomes more obvious with increasing QP values
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Classification of Body Language from Point Clouds using Neural Networks
No full textWhile neural networks are effective in classifying objects from highly structured data, their efficacy on unstructured point clouds has been more limited. In particular, their potential in classifying objects with subtle differences, such as body posture, has yet to be explored beyond simple gestures.Body language is an important type of communication as it enables people to ”speak” through their behaviors. However, the capability to understand body language varies under neu- rodivergent populations. From identifying common body poses, we could assist neurodivergent individuals in recognizing social cues from others or adjusting their own.The aim of this thesis is to explore how deep learning can understand various poses in non-verbal communication and determine its potential in body language assistance. We test the ability of a deep neural network to classify poses from point cloud distributions recorded from a LiDAR sensor. Implementing dual-dimension blocks to the network improved performance by an average of 25% relative to the baseline provided by the original model, while adding hand-crafted features on top of that led to a 2-3% increase in accuracy. Alongside the overall improvements with dual-dimension annotations, the proposed network leads to improvements across non-background classes by an average of 2%. Based on our results and adjustments, we show how our network can identify body language based on the pose’s gesture and direction
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Energy Optimization for Hybrid ARQ
No full textHybrid automatic repeat request (HARQ) \cite{costello1983error} plays an important role in providing reliable and efficient data transmission. In wireless communications, the wireless channel may vary fast, due to the mobility of the transmitter/receiver and the channel. Forward error correction (FEC) and automatic repeat request (ARQ) are two basic techniques to control errors. FEC employs error correction coding, by adding parity bits to the information bits, to combat channel errors. ARQ allows the receiver to request a retransmission of the packet when an error is detected in the received packet. HARQ gives protection to the wireless transmission by combining FEC and ARQ. In typical HARQ systems, redundancy is added to the information bits, and a retransmission is performed until either the packet is successfully decoded, or a maximum number of transmissions is reached.The motivation to optimize the energy consumption of HARQ is the high energy consumption of wireless communications on mobile devices. Wireless devices usually have a limited battery life, and wireless communications consume the majority of the battery energy of mobile devices. One example is that 3G and Wifi units consume more than 50\% of the energy for some smart phones \cite{tawalbeh2016studying}. Another example is that battery depletion has been identified as one of the primary factors that limit the lifetime of wireless sensor networks \cite{verdone2010wireless}.Previous works on HARQ mainly use information-theoretic approach, which assumes that the number of bits in each transmission round is sufficiently large. This assumption does not necessarily hold for actual codes with finite length. Therefore, in this dissertation, we consider HARQ with actual codes. We use turbo-coded HARQ, since turbo codes are well-known capacity-approaching codes \cite{berrou1993near} and widely used in standards such as 3GPP Long-Term Evolution (LTE) \cite{3gpp2007mulltiplexing}. We study the energy optimization for HARQ in two scenarios: the energy optimization for incremental redundancy (IR) HARQ, and the energy optimization for HARQ in wireless video transmission. For IR HARQ, each retransmission contains additional parity bits beyond those of the previous transmissions. For the first scenario, we consider different cases of channel state information (CSI) at the transmitter: the transmitter has no knowledge of any CSI, or knows the CSI in previous transmission rounds through a perfect feedback channel, or knows both current and previous CSI. The transmitter decides the forward error correction code rate based on the CSI it has. We minimize the energy consumption of turbo-coded HARQ, subject to a packet loss rate constraint. Numerical results show that the energy consumption of HARQ decreases when more CSI information is available at the transmitter. We also compare IR combining with both Chase combining and the system without combining, and IR combining yields the least energy consumption.For the second scenario, we formulate the problem as maximizing the video quality, subject to a constraint on the wireless transmission energy consumption. We consider multiple parameters in multiple layers in a wireless video transmission system: transmit power, alphabet size, FEC code rate, maximum number of transmissions and unequal video data importance. An analytical framework is proposed to include these parameters, which allows us to divide this problem into two sub-problems: data transmission and unequal error protection (UEP) for video content. The problem is tackled by solving the two sub-problems, which are done by exhaustive search and convex optimization, respectively. Simulations of different videos show that the proposed scheme outperforms methods using conventional data transmission and/or unequal error protection. For example, in the low SNR region, there is a total gain of 4.8 to 5.6dB on the peak signal-to-noise ratio of the received video compared to video transmission using conventional HARQ without any video UEP
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Design of a Digital Guitar Amplifier
No full textDigital amplifier modelling is an application of digital signal processing to recreate the sound of plugging an electric guitar into a traditional physical amplifier. Physical amplifiers can be impractical and unreliable for constant live use by professional musicians, and digital amplifiers have been developed to solve these problems. This paper explores my process of designing a digital guitar amplifier as well as an analysis of the final sound output
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Analysis and Practice of Nonverbal Communication and Attention in Autism with Virtual Reality Job Interviews Using Machine Learning
No full textAutism is a complex neurodevelopmental condition that influences how individuals act in social settings and process information. This condition is often characterized by differences in social communication that diverge from societal norms, typically viewed as the correct behavior. These behavioral mismatches may explain why only a small percentage of autistic individuals are employed, despite many seeking work. Although some companies are familiarizing their non-autistic (NA) employees with autism, most still lack neurodiversity hiring initiatives. To assist individuals who feel obliged to adjust their communication styles to fit NA norms, we designed virtual reality (VR) mock job interviews and developed algorithms for better behavioral tracking. We created a pipeline that can accurately detect head gestures using hidden Markov models and rate conversational engagement. Then, we combined Kalman filtering and a clustering algorithm to improve the built-in eye-tracking of a VR headset. Using enhanced eye-tracking, we explored how autism impacts gaze behavior. This was the first VR study to investigate the importance of conversational role in two-person job interviews. We extended the findings of previous non-immersive studies to immersive VR. The users liked our tool as a self-deliverable job interview practice opportunity. We then trained a neural network to predict head orientations in three-person virtual job interviews using a VR headset. Our model computed head rotation angles more accurately than conventional methods such as ray casting. The results aligned with our findings from two-person interviews. We observed how different neurotypes distribute their attention for different conversational roles, and how autism and external stimulants affect joint attention tendencies. We built a convolutional bidirectional long short-term memory model that can accurately identify user leans (forward or backward) based solely on a headset and its controllers. Previous studies have developed similar models for similar pose estimation tasks; however, none aimed to recognize leans during a conversation, which was shown to be a signal of attention.Lastly, we built a gaze behavior coaching framework that is more affordable than human coaching. Discussions with autistic individuals refined the coaching methodologies. After coaching, participants’ gaze behaviors generally approached the NA medians. This coaching framework was well-received
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