Search

Audio signals are information rich nonstationary signals that play an important role in our day-to-day communication, perception of environment, and entertainment. Due to its non-stationary nature, time- or frequency-only approaches are inadequate in analyzing these signals. A joint time-frequency (TF) approach would be a better choice to efficiently process these signals. In this digital era, compression, intelligent indexing for content-based retrieval, classification, and protection of digital audio content are few of the areas that encapsulate a majority of the audio signal processing applications. In this paper, we present a comprehensive array of TF methodologies that successfully address applications in all of the above mentioned areas. A TF-based audio coding scheme with novel psychoacoustics model, music classification, audio classification of environmental sounds, audio fingerprinting, and audio watermarking will be presented to demonstrate the advantages of using time-frequency approaches in analyzing and extracting information from audio signals.

In this paper, we explicitly analyze the performance effects of several orthogonal and bi-orthogonal wavelet families. For each family, we explore the impact of the filter order (length) and the decomposition depth in the multiresolution representation. In particular, two contexts of use are examined: compression and denoising. In both cases, the experiments are carried out on a large dataset of different signal kinds, including various image sets and 1D signals (audio, electrocardiogram and seismic). Results for all the considered wavelets are shown on each dataset. Collectively, the study suggests that a meticulous choice of wavelet parameters significantly alters the performance of the above mentioned tasks. To the best of authors’ knowledge, this work represents the most complete analysis and comparison between wavelet filters. Therefore, it represents a valuable benchmark for future works.

Electroencephalography (EEG) signals are commonly used in medical applications for prevention, diagnosis, and detection of neurological diseases. These EEG signals have also been used in designing brain computer interfaces for assistive technologies. For densely placed electrodes and long EEG recordings, a large amount of data needs to be stored, preferably in compressed form. This EEG signal compression is particularly required in an out-of-the-lab environment so that these signals are efficiently transmitted over wired/wireless communication channels. To this end, we propose a novel compression scheme for EEG signals, which exploits the intra-channel redundancy using motion compensated temporal filtering (MCTF) and discrete wavelet transform (DWT) based sub-band coding. In the pre-processing stage, multi-frame data is constructed such that each group of picture (GOP) contains information from a single channel of EEG data. This helps in removing the intra-channel redundancy. We apply MCTF on each GOP to exploit temporal redundancy, following which the DWT is applied on temporally decomposed frames to exploit spatial redundancy. Each spatio-temporal decomposed frame is assigned a bit budget for minimum distortion. For this purpose, we assign more bit budget to temporally decomposed low pass frames as compared to high pass frames. Spatio-temporal frames are then encoded at the assigned bit rate by using set partitioning in hierarchical tree (SPIHT) algorithm to create the bit stream. Our experimental results showed 4.5% and 2.4% reduction in distortion at the same data rate for BCI-3 and BCI-4 datasets, respectively. These results improve upon the reduction in data size achieved using state-of-the-art compression methods such as SPIHT and SPIHT with independent component analysis.

According to the circle-packing theorem, the packing efficiency of a hexagonal lattice is higher than an equivalent square tessellation. Consequently, in several contexts, hexagonally sampled images compared to their Cartesian counterparts are better at preserving information content. In this paper, novel mapping techniques alongside the wavelet compression scheme are presented for hexagonal images. Specifically, we introduce two tree-based coding schemes, referred to as SBHex (spirally-mapped branch-coding for hexagonal images) and BBHex (breadth-first block-coding for hexagonal images). Both of these coding schemes respect the geometry of the hexagonal lattice and yield better compression results. Our empirical results show that the proposed algorithms for hexagonal images produce better reconstruction quality at low bits per pixel representations compared to the tree-based coding counterparts for the Cartesian grid., Accepted Manuscript

Subband coding is a popular technique to achieve multichannel data compression and efficient data transmission in image and video communications. In this paper, we focus on designing a data-dependent image subband coder which divides the image into strongly (total) decorrelated and spectrally majorized subbands, which seeks to reduce the redundancy to achieve better compression and efficient transmission. To achieve strong decorrelation and spectral majorization between the subbands, we adopt a set of new iterative polynomial eigenvalue decomposition (PEVD) algorithms: sequential matrix diagonalization (SMD) and maximum element sequential matrix diagonalization (ME-SMD). Using this SMD-based PEVD approach, we design the data dependent subband coder (DDSC) for image subband coding. We compare the performance of the proposed SMD/ME-SMD algorithms with the existing DDSC methods like SBR2, SBR2C, K–L transform (KLT) coder and data independent subband coder (DISC)-based discrete wavelet transform (DWT) technique. To measure the performance, we use the parameters like coding gain, correlation coefficient, MSE (mean square error) and peak signal-to-noise ratio (PSNR). The presented simulation results for standard images in the absence of quantization show that the proposed SMD-based PEVD technique performs far better than the existing techniques.

This paper presents an improved image-adaptive watermarking technique. Two image watermarks are embedded in the high entropy 8 × 8 blocks of the host image. DWT is applied on these blocks using the principle of sub band coding. This decomposes the high entropy blocks into four sub band coefficients, wherein the approximation and vertical frequency coefficients are modeled using Gaussian (or Normal) distribution. The two watermarks are inserted in the host image using Adjustable Strength Factor (ASF). It is calculated adaptively using the fourth statistical moment known as kurtosis. A limited side information is also transmitted along with the watermarked image. This side information consists of high entropy block positions and Gaussian distribution parameters. To extract both watermarks from the received watermarked image, the high entropy block positions sent in the side information help in applying DWT to calculate the approximation and vertical frequency coefficients. Gaussian (or Normal) distribution is similarly used for modeling and calculating the distribution parameters. This helps the Maximum Likelihood (ML) decoder to recover the watermarks successfully using a statistical approach. Two important contributions are presented in this paper. Firstly, adjustable kurtosis values are used which improves the capacity and robustness of the proposed technique. Secondly, the proposed work is implemented on medical applications and gives better performance as compared to the existing methods. Further, the efficiency of the proposed work is evaluated by better simulation results using PSNR, NCC, SSIM and GMSD under different attacks. The technique is highly robust as watermarks survive under different attacks. This increases security and ensures copyright protection.

High data throughput during real-time vibration monitoring can easily lead to network congestion, insufficient data storage space, heavy computing burden, and high communication costs. As a new computing paradigm, edge computing is deemed to be a good solution to these problems. In this paper, perceptual hashing is proposed as an edge computing form, aiming not only to reduce the data dimensionality but also to extract and represent the machine condition information. A sub-band coding method based on wavelet packet transform, two-dimensional discrete cosine transform, and symbolic aggregate approximation is developed for perceptual vibration hashing. When the sub-band coding method is implemented on a monitoring terminal, the acquired kilobyte-long vibration signal can be transformed into a machine condition hash occupying only a few bytes. Therefore, the efficiency of condition monitoring can benefit from the compactness of the machine condition hash, while comparable diagnostic and prognostic results can still be achieved. The effectiveness of the developed method is verified with two benchmark bearing datasets. Considerations on practical condition monitoring applications are also presented.

Video compression is a very efficient method for storage and transmission of digital video signal. The applications include multimedia transmission, teleconferencing, videophone, high-definition television (HDTV), CD-ROM storages, etc. The hybrid coding techniques based on predictive and transform coding are the most popular and adopted by many video coding standards such as MPEG-1/2/4 [1] and H.261/H.263/H.264 [2, 3], owing to its high compression efficiency. In the hybrid coding system, the motion compensation, first proposed by Netravali and Robbins in 1997, plays a key role from the view point of coding efficiency and implementation cost [4-11]. A generic hybrid video coder is depicted in Figure 1. Fig. 1. A generic hybrid motion compensated DCT video coder. DCT Quan-tization De-Quan-tization IDCT Motion Compensation Motion Estimation Frame Buffer Variable Length Coder

This article proposes a lossy compression algorithm and scalable multispectral image coding—including blue, green, red, and near-infrared wavelengths—aimed at increasing image quality based on the amount of data received. The algorithm is based on wavelet subband coding and quantization, predictive multispectral image coding at different wavelengths, and the Huffman coding. The methodology was selected due to small satellites’ low data rate and a brief line of sight to earth stations. The test image database was made from the PeruSat-1 and LANDSAT 8 satellites in order to have different spatial resolutions. The proposed method was compared with the SPIHT, EZW, and STW techniques and subsequently submitted to a peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) evaluation; it showed better efficiency and reached compression ratios of 20, with a PSNR of 30 and an SSIM of approximately 0.8, depending on the multispectral image wavelength.

International audience; In this paper an audio coding scheme based on the empirical mode decomposition in association with a psychoacoustic model is presented. The principle of the method consists in breaking down adaptively the audio signal into intrinsic oscillatory components, called Intrinsic Mode Functions (IMFs), that are fully described by their local extrema. These extrema are encoded. The coding is carried out frame by frame and no assumption is made upon the signal to be coded. The number of allocated bits varies from mode to mode and obeys to the coding error inaudibility constraint. Due to the symmetry of an IMF, only the extrema (maxima or minima) of one of its interpolating envelopes are perceptually coded. In addition, to deal with rapidly changing audio signals, a stationarity index is used and when a transient is detected, the frame is split into two overlapping sub-frames. At the decoder side, the IMFs are recovered using the associated coded maxima, and the original signal is reconstructed by IMFs summation. Performance of the proposed coding is analyzed and compared to that of MP3 and AAC codecs, and the wavelet-based coding approach. Based on the analyzed mono audio signals, the obtained results show that the proposed coding scheme outperforms the MP3 and the wavelet-based coding methods and performs slightly better than the AAC codec, showing thus the potential of the EMD for data-driven audio coding.

This paper deals with the accuracy of algorithms for the detection of ventricular late potentials (VLP) in an electrocardiographic signal (ECG), being associated with malignant arrhytmias and possible cardiac death. VLP detection is strongly influenced by signal noise. The objective of this paper is to define a denoising algorithm improving the VLPs detection. The method uses wavelet denoising, subband coding, unfortunately introducing heavy linear distortions. Therefore, an equalisation filter has been properly designed, in order to cancel the distortions. The algorithm has been implemented and successfully verified using MATLAB. Then, it has been implemented on Altera's FPGA and then verified on the evaluation board DE1-SoC. On-board processed results and theoretical results are consistent, validating the algorithm. The results show that the algorithm capability to be implemented as programmable hardware. It also could be used for upgrading ECG devices reliability in the field of heart diseases prevention.

Transform, as one of the most important modules of mainstream video coding systems, seems very stable over the past several decades. However, recent developments indicate that bringing more options for transform can lead to coding efficiency benefits. In this paper, we go further to investigate how the coding efficiency can be improved over the state-of-the-art method by adapting a transform for each block. We present a variable block-sized signal-dependent transforms (SDTs) design based on the High Efficiency Video Coding (HEVC) framework. For a coding block ranged from $4\times4$ to $32\times32$ , we collect a quantity of similar blocks from the reconstructed area and use them to derive the Karhunen–Loeve transform. We avoid sending overhead bits to denote the transform by performing the same procedure at the decoder. In this way, the transform for every block is tailored according to its statistics, to be signal-dependent. To make the large block-sized SDTs feasible, we present a fast algorithm for transform derivation. Experimental results show the effectiveness of the SDTs for different block sizes, which leads to up to 23.3% bit-saving. On average, we achieve BD-rate saving of 2.2%, 2.4%, 3.3%, and 7.1% under AI-Main10, RA-Main10, RA-Main10, and LP-Main10 configurations, respectively, compared with the test model HM-12 of HEVC. The proposed scheme has also been adopted into the joint exploration test model for the exploration of potential future video coding standard.

Screen content coding plays an important role in many applications. Conventional reference picture management (RPM) strategies developed for natural content may not work well for screen content. This is because many regions in screen content remain static for a long time, causing a lot of repetitive contents to stay in the decoded picture buffer. The repetitive contents are not conducive to inter prediction, but still occupy valuable memory. This paper proposes a diversity-based RPM scheme for screen content coding. The concept of diversity is introduced for the reference picture set (RPS) to help formulate the RPM problem. By maximizing the diversity of RPS, more potentially better predictions are provided. Better compression performance can then be achieved. Meanwhile, the proposed scheme is nonnormative and compatible with existing video coding standards, such as High Efficiency Video Coding. The experimental results show that, for low delay screen content coding, the bit saving of the proposed scheme is 4.9% on average and up to 13.7%, without increasing encoding time.

The design of a multi-channel cosine-modulated filter bank (CMFB) using bacterial foraging optimization (BFO) is proposed. In this work, the canonic signed digit (CSD) technique is used to ...

Subband coding has recently emerged as the leading standardization candidate in audio/video/image compression, echo cancellation, radar, image analysis, communications, medical imaging etc. Due to this, Discrete Wavelet Transform has gained much attention in memory efficient technique for handling large amounts of data. The implicit overlapping and variable-length basis functions from wavelets produce smoother and more pleasant reconstructions. Data compression, bit rate reduction is done by implementing memory efficient Discrete Wavelet Transform based on Coextensive Distributive Computation on FPGA. In this work Low complexity DWT Architecture which utilizes the Look-Up Table, using Shift register FPGA structure is developed to develop a filter bank without multiplier which is the important components in a DWT structure. This results in better space usage and reduction in the area size. Filter bank construction is done using DB-4 Daubechies 9/7 wavelets. This paper presents a consistent performance, good operating speed and area efficient DWT processor along with best utilization of resources available on target FPGA. The proposed Discrete Wavelet Transform System is implemented on Xilinx xc2vp30-7-ff896 Field Programmable Gate Array with highest operating frequency of 141.055MHz.

Efficient coding of speech and audio in a distributed system requires that quantization errors across nodes are uncorrelated. Yet, with conventional methods at low bitrates, quantization levels become increasingly sparse, which does not correspond to the distribution of the input signal and, importantly, also reduces coding efficiency in a distributed system. We have recently proposed a distributed speech and audio codec design, which applies quantization in a randomized domain such that quantization errors are randomly rotated in the output domain. Similar to dithering, this ensures that quantization errors across nodes are uncorrelated and coding efficiency is retained. In this paper, we improve this approach by proposing faster randomization methods, with a computational complexity of $\mathcal O(N\log N)$ . The presented experiments demonstrate that the proposed randomizations yield uncorrelated signals, that perceptual quality is competitive, and that the complexity of the proposed methods is feasible for practical applications.

Wiener filtering, which has been widely used in the field of image restoration, is statistically optimal in the sense of mean square error. The adaptive loop filter in video coding inherits the design of Wiener filters, and has been proved to achieve significant improvement on compression performance by reducing coding artifacts and providing high-quality references for subsequent frames. To further improve the compression performance via filtering technique, we explore the factors that may hinder the potential performance of Wiener-based filters, and propose a near-optimal filter learning scheme for high-efficiency image coding. Based on the analyses, we observe that the foremost factor affecting the performance of Wiener-based filters is the divergence of statistical characteristics of training samples, instead of the filter taps or shapes. In view of this, we propose an iterative training method to derive the near-optimal Wiener filter parameters by simultaneously labeling sample categories at the pixel level. These parameters are compressed and transmitted to the decoder side to improve the quality of decoded images by reducing the coding artifacts. Experimental results show that the proposed scheme achieves significant bitrate savings compared with high-efficiency video coding in high-bitrate intra coding scenario.

Low-delay hierarchical coding structure (LD-HCS), as one of the most important components in the latest High Efficiency Video Coding (HEVC) standard, greatly improves coding performance. It groups consecutive P/B frames into different layers and encodes them with different quantization parameters (QPs) and reference mechanisms in such a way that temporal dependency among frames can be exploited. However, due to varying characteristics of video contents, temporal dependency among coding units differs significantly from each other in the same or different layers, while a fixed LD-HCS scheme cannot take full advantage of the dependency, leading to a substantial loss in coding performance. This paper addresses the temporally dependent rate distortion optimization (RDO) problem by attempting to exploit varying temporal dependency of different units. First, the temporal relationship of different frames under the LD-HCS is examined, and hierarchical temporal propagation chains are constructed to represent the temporal dependency among coding units in different frames. Then, a hierarchical temporally dependent RDO scheme is developed specifically for the LD-HCS based on a source distortion propagation model. Experimental results show that our proposed scheme can achieve 2.5% and 2.3% BD-rate gain in average compared with the HEVC codec under the same configuration of P and B frames, respectively, with a negligible increase in encoding time. Furthermore, coupled with QP adaption, our proposed method can achieve higher coding gains, e.g., with multi-QP optimization, about 5.4% and 5.0% BD-rate saving in average over the HEVC codec under the same setting of P and B frames, respectively.

Object-based audio coding is the main technique of audio scene coding. It can effectively reconstruct each object trajectory, besides provide sufficient flexibility for personalized audio scene reconstruction. So more and more attentions have been paid to the object-based audio coding. However, existing object-based techniques have poor sound quality because of low parameter frequency domain resolution. In order to achieve high quality audio object coding, we propose a new coding framework with introducing the non-negative matrix factorization (NMF) method. We extract object parameters with high resolution to improve sound quality, and apply NMF method to parameter coding to reduce the high bitrate caused by high resolution. And the experimental results have shown that the proposed framework can improve the coding quality by 25%, so it can provide a better solution to encode audio scene in a more flexible and higher quality way.

Most mobile video applications require the encoder to have low complexity. Wyner-Ziv (WZ) video coding removes complex motion estimation from the encoder, and provides error resilience from the embedded channel coding module. WZ video coding is regarded as a promising encoder for wireless video systems. Most WZ video coding based on channel codes is a practical implementation of the binning schemes. In this work, we present a novel two-tier binning scheme that consists of the inner and outer structure, for improving rate-distortion performance. First, we develop a Raptor coding with side information to construct the inner binning structure, which provides a lower rate. Second, for the outer binning, we model the WZ video coding architecture as a multi-access channel, so that we can exploit the property of channel capacity. Third, we exploit the duality property of WZ video coding. Based on such a property, both the primal and dual solutions are subsequently provided in this study. For the primal problem of distortion minimization, we develop dynamic programming to find the optimal binning policy, whereas for the dual problem of capacity maximization, we devise a near sum-capacity binning algorithm. The objective is to lower the coding rate with lower complexity. Experimental results showed that when compared with the state-of-the-art coding, the decoding performance and the quality of our proposed method were respectively enhanced. Besides, we observed that the decoding distortion was reduced through the proposed outer binning, while the proposed inner binning based on Raptor coding by jointly considering side information (SI) lead to a low bitrate when a target decoding quality was specified. Such findings have substantiated the effectiveness of our method.

Professional applications like telemedicine often require scalable lossless coding of sensitive data. 3-D subband coding has turned out to offer good compression results for dynamic CT data and additionally provides a scalable representation in terms of low- and highpass subbands. To improve the visual quality of the lowpass subband, motion compensation can be incorporated into the lifting structure, but leads to inferior compression results at the same time. Prior work has shown that a denoising filter in the update step can improve the compression ratio. In this paper, we present a new processing order of motion compensation and denoising in the update step and additionally introduce a second denoising filter in the prediction step. This allows for reducing the overall file size by up to 4.4%, while the visual quality of the lowpass subband is kept nearly constant.

Recently, a lot of deep learning model successful in taking over conventional methods in speech processing fields. Vector quantization is a popular technique to reduce the amount of speech data before transmitting. The conventional vector quantization method is based on the mathematical model. Last few years, the Vector Quantized Variational AutoEncoder has been proposed for an end-to-end vector quantization based on deep learning techniques. In this paper, we investigate the sub-band quantization in the Vector Quantized Variational AutoEncoder. This model can concentrate on specific frequency bands to assign more bits and leave the unnecessary band with few bits. Experimental results show the efficiency of the proposed quantization method for the spectral envelope parameters of the high-quality vocoder that operates at 48 kHz sampling frequency named WORLD vocoder. At the same four target bit rates, the sub-band Vector Quantized Variational AutoEncoder can reduce the Log Spectral Distortion around 0.93 dB in average.

This proposed paper presents a design approach for two-channel quadrature mirror filter (QMF) bank using improved artificial bee colony algorithm. Artificial bee colony algorithm has been modified by fine tuning some existing control parameters. Here QMF bank design is used for a lowpass prototype FIR filter design problem. The objective function is the minimizing of mean square error of magnitude responses between desired and designed low-pass prototype FIR filters. The objective function is minimized by using the modified ABC algorithm yielding improved results. Two design examples are also presented to show the efficiency of the proposed method over the existing methods in the literature. The results of the proposed design approach have also been compared with that of the produced results in the literature.

Filter bank based convolutional networks (FBCNs) enable efficient separable multiscale and multidirectional decomposition with a convolutional cascade of 1-D radial and directional filter banks. In this paper, we propose a two-stage subband coding framework for FBCN analysis coefficients using a SPIHT-like algorithm and subsequent primitive-based adaptive arithmetic coding (AAC). The SPIHT-like algorithm extends spatial orientation tree to exploit inter-subband dependency between subbands of different scales and directions. Mutual information is estimated for information-theoretical measurement to formulate such dependencies. Various primitives are designed adaptively encode the generated bitstream by fitting its varying lists and passes. Neural networks are leveraged to improve probability estimation for AAC, where nonlinear prediction is made based on contexts regarding scales, directions, locations and significance of analysis coefficients. Experimental results show that the proposed framework improves the lossy coding performance for FBCN analysis coefficients in comparison to the state-of-the-arts subband coding schemes SPIHT.

Fractional-pel accurate motion is widely used in video coding. For subband coding, fractional-pel accuracy is challenging since it is difficult to handle the complex motion field with temporal transforms. In our previous work, we designed integer accurate motion-adaptive transforms (MAT) which can transform integer accurate motion-connected coefficients. In this paper, we extend the integer MAT to fractional-pel accuracy. The integer MAT allows only one reference coefficient to be the lowband coefficient. In this paper, we design the transform such that it permits multiple references and generates multiple lowband coefficients. In addition, our fractional-pel MAT can incorporate a general interpolation filter into the basis vector, such that the highband coefficient produced by the transform is the same as the prediction error from the interpolation filter. The fractional-pel MAT is always orthonormal. Thus, the energy is preserved by the transform. We compare the proposed fractional-pel MAT, the integer MAT, and the half-pel motion-compensated orthogonal transform (MCOT), while HEVC intra coding is used to encode the temporal subbands. The experimental results show that the proposed fractional-pel MAT outperforms the integer MAT and the half-pel MCOT. The gain achieved by the proposed MAT over the integer MAT can reach up to 1 dB in PSNR.

In this paper, we propose a hybrid Wyner-Ziv video coding structure that combines conventional motion predictive video coding and Wyner-Ziv video coding to eliminate the feedback channel, which is a major practical problem in applications using the Wyner-Ziv video coding approach. The proposed method divides a hybrid frame into two regions. One is coded by a motion predictive video coder, and the other by the Wyner-Ziv coding method. The proposed encoder estimates side information with low computational complexity, using the coding information of the motion predictive coded region, and estimates the number of syndrome bits required to decode the region. The decoder generates side information using the same method as the encoder, which also reduces the computational complexity in the decoder. Experimental results show that the proposed method can eliminate the feedback channel without incurring a significant rate-distortion performance loss.

The screen content coding (SCC) Extensions of high efficiency video coding (HEVC) employs in-loop adaptive color-space transform (ACT) technique to explore the inter-color-component redundancy, i.e., statistical redundancy among different color components. In ACT, the prediction residual signal is adaptively converted into a different color space, i.e., YCgCo. The rate-distortion criteria is employed to decide whether to code the residual signal in the original color space or YCgCo color space. Typically, the inter-color-component correlation could be reduced when ACT is enabled. The residual signal after possible color-space conversion is then coded, following the existing HEVC framework, i.e., transform if necessary, quantization and entropy coded. This paper describes the design of ACT from several points of view, from theoretical analysis to implementation details. Experimental results are also provided to demonstrate the significant coding gains of ACT in the HEVC SCC Extensions.

We present an in-depth analysis of the problem of lossy compression of binary sources in the presence of correlated side information, where the correlation is given by a generic binary asymmetric channel and the Hamming distance is the distortion metric. Our analysis is motivated by systematic rate-distortion gains observed when applying asymmetric correlation models in Wyner-Ziv video coding. Firstly, we derive for the first time the rate-distortion function for conventional predictive coding in the binary-asymmetric-correlation-channel scenario. Secondly, we propose a new bound for the case where the side information is only available at the decoder - Wyner-Ziv coding. We conjecture this bound to be tight. We show that the maximum rate needed to encode as well as the maximum rate-loss of Wyner-Ziv coding relative to predictive coding correspond to uniform sources and symmetric correlations. Importantly, we show that the upper bound on the rate-loss established by Zamir is not tight and that the maximum value is actually significantly lower. Moreover, we prove that the only binary correlation channel that incurs no rate-loss for Wyner-Ziv coding compared to predictive coding is the Z-channel. Finally, we complement our analysis with new compression performance results obtained with our state-of-the-art Wyner-Ziv video coding system.

There is a considerable performance gap between the current scalable audio coding schemes and a nonscalable coder operating at the same bitrate. This suboptimality results from the independent coding of the layers in these systems. One of the aspects that plays a role in this suboptimality is the entropy coding. In practical audio coding systems including MPEG advanced audio coding (AAC), the transform domain coefficients are quantized using an entropy-constrained quantizer. In MPEG-4 scalable AAC (S-AAC), the quantization and coding are performed separately at each layer. In case of Huffman coding, the redundancy introduced by the entropy coding at each layer is larger at lower quantization resolutions. Also, the redundancy for the overall coder becomes larger as the number of layers increases. In fact, there is a tradeoff between the overall redundancy and the fine-grain scalability in which the bitrate per layer is smaller and more layers are required. In this paper, a fine-grain scalable coder for audio signals is proposed where the entropy coding of a quantizer is made scalable via joint design of entropy coding and quantization. By constructing a Huffman-like coding tree where the internal nodes can be mapped to the reconstruction points, the tree can be pruned at any internal node to control the rate-distortion (RD) performance of the encoder in a fine-grain manner. A set of metrics and a trellis-based approach is proposed to create a coding tree so that an appropriate path is generated on the RD plane. The results show the proposed method outperforms the scalable audio coding performed based on reconstruction error quantization as used in practical systems, e.g., in S-AAC.

This paper proposes a pre-processing algorithm to improve the coding efficiency of perceptual video coding. First, an input image is decomposed into multiple sub-bands through linear sub-band decomposition. Then, the sub-bands that have low visual sensitivity are suppressed by assigning small gains to them. Experimental results show that if the proposed algorithm is adopted for pre-processing in a High Efficiency Video Coding (HEVC) encoder, it can provide significant bit-saving effects of approximately 12% in low delay mode and 9.4% in random access mode.

Available distributed video coding codecs are mostly based on decoder rate control scheme where the parity bits for decoding can be achieved over a feedback channel. Meanwhile, the frequent requests over feedback channel increase the transmission delay. The feedback-free distributed video coding, relying on encoder rate control in literatures, has overcome the aforementioned shortcoming. However, when performing parity bitrate estimation and other operations, the feedback-free distributed video coding systems based on bit-plane usually require high precision of bitrate estimation and high quality of side information at the encoder. In this paper, we propose a frame-level distributed video coding system based on encoder rate control. The innovations include three parts: 1) an adaptive coding mode selection algorithm is proposed, which utilizes both temporal and spatial correlation and reduces the complexity of encoder; 2) a bit-plane rearrangement method is adopted, which makes the coding rate on each bit-plane homogeneous and effectively reduces the accuracy requirement of the parity bitrate prediction and improves the efficiency of rate estimation; 3) a frame-level parity bitrate estimation scheme is presented to enhance the efficiency of rate estimation on the basis of a look-up table. Numerical results verify that the proposed scheme remarkably improves the rate distortion performance of distributed video coding at low bitrate.

In this paper, a residual differential pulse code modulation (RDPCM) coding technique using a weighted linear combination of neighboring residual samples is proposed to provide coding efficiency in the screen content video coding. The RDPCM performs the sample-based prediction of residue to reduce spatial redundancies. The proposed method uses the $l_1$ optimization in the weight derivation by considering the statistical characteristics of graphical components in videos in an intracoding. Specifically we use the least absolute shrinkage and selection operator to derive the weights because the solution is accurate in high variance residue. Furthermore, we enhance parallelism in a line processing by restricting the support to the row-wise prediction to above samples or the column-wise prediction to the left samples. The proposed method uses an explicit RDPCM scheme, so a coding mode determined by rate-distortion optimization is transmitted to a decoder. For coding the overhead, we develop a context design in CABAC based on correlation between an intraprediction direction and an RDPCM prediction mode. It is demonstrated with the experimental results that the proposed method provides a significant coding gain over the state-of-the-art reference codec for screen content video coding.