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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.

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.

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.

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.

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.

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.

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.

A multiview video plus depth format was introduced to support 3D depth perception and arbitrary view generation. To achieve high coding performance in AVC-compatible 3D video coding (3D-AVC), we herein propose an efficient depth map coding method that includes synthesis-based depth residual coding and filtering. The synthesis-based depth residual coding method adaptively encodes a residual by predicting synthesis distortion from spatial complexity of the corresponding texture, in a nonnormative way. In order to compensate for possible error from the proposed coding method, synthesis-based depth filtering is additionally conducted on the reconstructed depth map. Experimental results demonstrate that the proposed method reduces the bit rates by 4.4% and 4.8%, compared with the original method for the decoded and synthesized PSNRs, respectively, while satisfying the compatibility with the current 3D-AVC.

The presence of Ventricular Late Potentials (VLP) in an Electrocardiographic signal (ECG) is associated with possible sudden cardiac death, caused by malignant arrhythmia. An effective VLP detection is strongly influenced by the signal noise, thus the ECG needs to be denoised before VLP detection. The objective of this paper is to define a denoising algorithm for enhancing VLP detection in ECGs, useful for implementation on a Field Programmable Gate Array device (FPGA). The method described uses wavelet denoising, implemented as subband coding. The drawbacks of this method are heavy linear distortions undergone by the analyzed signal. This disadvantage is overcome by using an equalization filter for canceling the introduced distortions. The algorithm is defined using MATLAB, then its hardware implementation is designed and simulated in Altera's Quartus software. The synthesized hardware is then verified on the evaluation board DE1-SoC by Terasic, mounting a Cyclone V Intel - Altera FPGA chip. On board processed results and theoretical results are consistent, validating the effectiveness of the designed hardware. Results show that such design could be used for upgrading ECG devices reliability in the field of heart diseases prevention.

High-resolution image compression remains a hot research topic today due to the widespread use of high-definition displays in computers and mobile devices. Conventionally, there are two distinct categories in image compression: 1) Subband coding based on multi-level discrete wavelet transform (DWT); 2) Hybrid coding based on block prediction and discrete cosine transform (DCT). Wavelet image compression has delivered a much higher coding gain over the classical DCT-based JPEG. More recently, through using a delicate yet innovative block prediction scheme before DCT, HEVC Intra coding has delivered an impressive coding gain over JPEG 2000, where the coding gain is measured by the peak signal-to-noise ratio (PSNR) gain at the same bit rate. However, it is hard to integrate a block prediction scheme with wavelet transform. A block prediction error image could be statistically quite different from an ordinary image. In the paper, we focus on developing a wavelet image coder SLCCA Plus, which is a much-enhanced version of SLCCA. The enhancement involves quantization, cluster filtering, shared-boundary-zero handling and context model design for adaptive arithmetic coding. The experiments on standard images of $512\times 512$ are conducted to compare the proposed SLCCA Plus to JPEG 2000 and SLCCA. On average, SLCCA Plus achieves 7% bit saving over JPEG 2000 and 4% over SLCCA.

Automated speaker recognition system is extremely important in areas such as Forensic and Defence. Performance of an automated speaker recognition system is dependent on feature extraction and classification. As speech is a non stationary signal and most of the information is present in the low frequency region, sub band coding or multi resolution analysis is favored. Empirical Mode Decomposition (EMD) is used for decomposing the signal and pitch, period, number of peaks, mean, standard deviation, skewness, kurtosis, energy, zero crossing rate, second order moment and third order moment are used for aggregating these coefficients. Adaptive Resonance Theory (ART) is chosen as the classifier. Sensitivity is the metric used for studying the performance of the proposed technique. From the results, it is also proved that the proposed ART based speaker recognition system using Empirical Mode Decomposition parameters gives 100% sensitivity for the acquired research database.

Intra-prediction is employed in block-based image coding to reduce energy in the prediction residual before transform coding. Conventional intra-prediction schemes copy directly from known pixels across block boundaries as prediction. In this letter, we first cluster differences between neighboring pixel pairs. Then, for each pixel pair, we add the cluster mean to the known pixel for prediction of the neighboring unknown pixel. The cluster indices are transmitted per block, allowing the decoder to mimic the same intra-prediction. We then propose an optimized transform for the prediction residual, based on a generalized version of previously developed Graph Fourier Transform (GFT). Experimental results show that our generalized intra-prediction plus transform coding outperforms combinations of previous intra-prediction and ADST coding by 2.5 dB in PSNR on average.

The target of 3D video coding is to compress Multiview Video plus Depth (MVD) format data, which consist of a texture image and its corresponding depth map. In the MVD format, the depth map plays an important role for successful services in 3D video applications, because it enables the user to experience 3D by generating arbitrary intermediate views. The depth map has a strong correlation with its associated texture data, so it can be utilized to improve texture coding efficiency. This paper introduces a novel and efficient depth-based texture coding scheme. It includes depth-based motion vector prediction, block-based view synthesis prediction, and adaptive luminance compensation, which were adopted in an AVC-compatible 3D video coding standard. Simulation results demonstrate that the proposed scheme reduces the total coding bitrates of texture and depth by 19.06% for the coded PSNR and 17.01% for the synthesized PSNR in a P-I-P view prediction structure, respectively.

The most difficult challenge in developing a high-efficiency embedded image processing system relates to how to execute complicated Digital Signal Processing (DSP) algorithm with embedded processor, especially when processing high-resolution images. A whole system may start to run very slowly because of the large volumes of data being processed and the complexity of the algorithm, rendering it unable to achieve real-time processing. Executing motion detection in low-resolution image was already proved to be able to reduce the loading of data manipulation and to be able to help filter out noise and fake motion. However, smoothing filter pointed out in the work will affect the quality of image. Although the LL-band signal of a 2D Discrete Wavelet Transform (DWT) domain can retain the main part of the energy and information of the original image, a complex algorithm is more suitable for the application of a low-pass filter. Symmetric Mask-based DWT (SMDWT) has the advantage of reduced complexity, regular signal coding, and independent subband coding processing. In this paper, we use a Particle Swarm Optimization (PSO) approach to automatically evolve the multiplierless 2D SMDWT filters hardware architecture. The architecture employs only shift-and-addition operations to replace the complex floating-point multiplication and division operations. It can use shift-and-add based SMDWT filter to decompose a low-resolution image and do real-time image processing system design with low-resolution image processing technique.

The high-efficiency video coding (HEVC) standard for three-dimensional (3D) video, which is called 3D-HEVC, improves the compression efficiency for high resolution 3D video sequences. In this study, three coding time reduction algorithms, namely fast coding unit partition, fast prediction unit (PU) partition and search range reduction, are proposed for inter-prediction of 3D-HEVC by referring to the depth edge information. The pixels in one object should have the same motion characteristics. By detecting depth edge, we can easily classify the regional pixels if they are in the same object. Thus, in the encoding stage, the sizes of coding and PUs can be determined by the proposed fast coding unit partition and fast PU partition methods, respectively. Furthermore, the range of motion search can be effectively reduced by the proposed search range reduction method. Experimental results show that the proposed depth edge-based motion search algorithms can reduce the coding time of inter-prediction by up to 62% on average with negligible quality degradation.

Applications involving indirect interpersonal communication, such as collaborative design/assembly/exploration of physical objects, can benefit strongly from the transmission of contact-based haptic media, in addition to the more traditional audiovisual media. Inclusion of haptic media has been shown to improve immersiveness, task performance, and the overall experience of task execution. While several decades of research have been dedicated to the acquisition, processing, coding, and display of audio and video streams, similar aspects for haptic streams have been addressed only recently. Simultaneous masking is a perceptual phenomenon widely exploited in the compression of audio data. In the first part of this paper, to the best of our knowledge, we present first-time empirical evidence for masking in the perception of wideband vibrotactile signals. Our results show that this phenomenon for haptics is very similar to its auditory analog. Signals closer in frequency to a powerful masker ( 25 dB above detection threshold) are masked more strongly (peak threshold-shifts of up to 28 dB) than those away from the masker (threshold-shifts of 15–20 dB). The masking curves approximately follow the masker's spectral profile. In the second part of this paper, we present a bitrate scalable haptic texture codec, which incorporates the masking model and describe its subjective and objective performance evaluation. Experiments show that we can drive down the codec output bitrate to a very low value of 2.3 kbps, without the subjects being able to reliable discriminate between the codec input and distorted output texture signals.

This paper addresses high performance depth coding in 3D video by making good use of its coded texture video counterpart. The relationship between the depth and its associated texture video in terms of coding mode and motion vector is carefully examined. Our statistical study suggests that the skip-coding mode and its associated motion vectors in the coded texture can be shared for depth coding by saving bit rate at the cost of little increase of distortion, which subsequently results in a nonsequential coding of the depth map. In this sense, coding/prediction of a block can be performed using the skip-coded blocks below and right, which are not available in the conventional sequential coding, thus producing the so-called omnidirectional blocks predicted in the intra-coding by making the best use of (at most) four neighboring blocks. Moreover, in view of the depth-texture structure similarity, a depth-texture cooperative clustering-based prediction method is proposed for cluster-based depth prediction in the intra-coding, which exploits the structure similarity for the current coding block and its neighboring pixels around the block. On the other hand, some large prediction errors may be present for the depth-texture misaligned pixels, which may greatly compromise the coding performance. To deal with these large residuals induced by the depth-texture misalignment, a simple yet effective detection and rectification approach is incorporated in the proposed depth coding scheme. Experimental results show that our proposed depth coding scheme achieves superior rate-distortion performance compared with other relevant coding methods.

Digital image processing refers to the process of digital images by means of digital computer. The main application area in digital image processing is to enhance the pictorial data for human interpretation. In image acquisition some of the unwanted information is present that will be removed by several preprocessing techniques. Filtering helps to enhance the image by removing noise. The aim of this paper is to demonstrate the lowpass and highpass filtering techniques, however they are the filtering techniques used in Fourier and Wavelet Transformations. In Wavelet Transform these two filters play an important role in reconstructing the original image by using subband coding. Lowpass filter will produce a Gaussian smoothing blur image, in the other hand, high pass filter will increase the contrast between bright and dark pixel to produce a sharpen image. General Terms Digital image processing, Image enhancement.

In this chapter, the two-channel filter bank is considered. Fundamental concept of filter bank theory and the analysis/synthesis configuration and different solutions are revisited. Such filter structures have been studied in many applications including subband coding, speech processing, image compression and eventually in pansharpening. This chapter is dedicated to the pansharpening (i.e., combining remotely sensed data at different resolution) application. In this context, a special structure of filter bank is introduced in Hallabia et al.(2016) in order to extract the high-frequency details from the panchromatic (PAN) image and transfer them into the up-sampled multispectral (MS) images. Based on the physics of the imaging sensor, the low-pass analysis filter is assumed to approximate the modulation transfer function (MTF). Under the perfect reconstruction property, the complementary high-pass filter (used to extract the high-frequency details) is conceived as the result of an optimization procedure. A qualitative and quantitative comparative study is discussed.

In-loop filters are widely employed in video coding standards to improve coding efficiency by reducing quantization noise. However, only local spatial correlation of video frames is exploited in existing in-loop filters, which limits the improvement of video coding efficiency. In this paper, a higher order transform domain filter (HOTDF) is proposed to further reduce quantization noise. The filter fully exploits non-local spatial correlation by grouping similar patches and sparsely representing them in a higher order transform domain. Then quantization noise can be well removed by preserving large transform coefficients conveying majority of original signals and discarding the remaining smaller ones mainly caused by quantization. Experimental results show that compared with HM16.0, the proposed algorithm achieves 1.9%, 3.0% and 2.5% BD-rate reduction for luminance component on average for all intra, low delay P and random access configurations, respectively.

In October 2015, MPEG and ITU jointly launched the Joint Video Exploration Team (JVET) to prepare the next generation of video coding standard beyond HEVC. The tools added over HEVC, grouped into the Joint Exploration Model 3.0 (JEM-3.0), achieve over 25% of coding gains compared to HEVC for the Random-Access mode. In inter-coding mode, the transform stage consists in the Adaptive Multiple Transforms (AMT) which puts classical DCT-2 in competition with alternative transforms. In this paper, it is proposed to improve the AMT stage in inter-coding.

In this paper, we present a novel pseudo sequence based 2-D hierarchical reference structure for light-field image compression. In the proposed scheme, we first decompose the light-field image into multiple views and organize them into a 2-D coding structure according to the spatial coordinates of the corresponding microlens. Then we mainly develop three technologies to optimize the 2-D coding structure. First, we divide all the views into four quadrants, and all the views are encoded one quadrant after another to reduce the reference buffer size as much as possible. Inside each quadrant, all the views are encoded hierarchically to fully exploit the correlations between different views. Second, we propose to use the distance between the current view and its reference views as the criteria for selecting better reference frames for each inter view. Third, we propose to use the spatial relative positions between different views to achieve more accurate motion vector scaling. The whole scheme is implemented in the reference software of High Efficiency Video Coding. The experimental results demonstrate that the proposed novel pseudo-sequence based 2-D hierarchical structure can achieve maximum 14.2% bit-rate savings compared with the state-of-the-art light-field image compression method.

In recent years the satellite communication has been growing for the development of rapid and efficient techniques for the storage and transmission of satellite images. The science of reducing the number of bits required to represent the image is called image compression. The aim of this paper is to reduce the size of the image, while transmitting the original image. This reduced image is called as compressed image. The compressed image is being transmitted and it is reconstructed in the receiving side so that the compressed image is being decompressed to obtain the original image. And also transmission of such satellite image increases communication accuracy but requires less bandwidth. In most systems, the amount of intimation that the user wishes to communicate or store necessities some form of compression for efficient and reliable use of communication or storage system. Although several well-known compression techniques exist, many of them require computationally intensive algorithm. In addition to that many compression techniques introduce unwanted attribute such as loss of intimation. This is a major problem in satellite imaging where image degradation may be critical. By considering the above, this paper is to use the wavelet to improve the compression ratio as well as visual quality which is implemented by the famous algorithm called sub band coding and decoding algorithm in MATLAB 7.1 software tool.

A novel and universal coding artifact reduction method is introduced.The coding artifact reduction is viewed as coding loss recovery.The coding loss is recovered from the compressed signal.Experimental results on four codecs demonstrate the effectiveness. Coding artifacts are annoying in highly compressed signals. Most of the existing artifact reduction methods are designed for one specific type of artifacts, codecs, and bitrates, which are complex and exclusive for one type of artifact reduction. Since both the compressed image/video and the coding error contain information of the original signal, they are highly correlated. Therefore, we try to recover some lost data based on the correlation between the compressed signal and the coding error, and introduce a novel and universal artifact reduction method. Firstly, according to the spatial correlation among pixels, a pixel-adaptive anisotropic filter is designed to reconstruct the distorted signal. Next, a globally optimal filter is designed to further recover the coding loss. Experimental results demonstrate that within an extensive range of bitrates, the proposed method achieves about 0.8dB, 0.45dB, 0.3dB, and 0.2dB on average of PSNR improvement for JPEG, MPEG4, H.264/AVC, and HEVC compressed signals, respectively.

This paper presents an advanced screen content coding solution using color table and index map (cTIM) method. This cTIM scheme is implemented in the range extensions of high-efficiency video coding (HEVC-RExt) as an additional tool of intracoding to complement conventional spatial angular prediction to better exploit the screen content redundancy. For each coding unit, a number of major colors will be selected to form the color table, then the original pixel block is translated to the corresponding index map. A 1D or hybrid 1D/2D string match scheme is introduced to derive matched pairs of index map for better compression. Leveraging the color distribution similarity between neighboring image blocks, color table merge is developed to carry it implicitly. For those blocks that color table has to be signaled explicitly, intertable color sharing and intratable color differential predictive coding are applied to reduce the signaling overhead. Extensive experiments have been performed and they have demonstrated the significant coding efficiency improvement over conventional HEVC-RExt, resulting in 26%, 18%, and 15% bit rate reduction at lossless case and 23%, 19%, and 13% Bjontegaard Delta-rate improvement at lossy scenario of typical screen content with text and graphics, for respective all intra, random access, and low-delay using B picture encoder settings. Detailed performance study and complexity analysis (as well as the comparison with other algorithms) have been included as well to evidence the efficiency of proposed algorithm.

A new lossless image coding method competitive with the best known image coding techniques in terms of efficiency and complexity is suggested. It is based on adaptive color space transform, adaptive context coding, and improved prediction of pixel values of image color components. Examples of application of the new algorithm to a set of standard images are given and comparison with known algorithms is performed.

In this paper, a distributed image coding scheme for multi-view video through an efficient generation of side information is proposed. A distributed video coding technique corrects the errors in the side information, which is generated with the original image, by using the channel coding technique at the decoder. Therefore, the more correct the generated side information is, the better the performance of distributed video coding. The proposed technique is to apply the distributed video coding schemes to the image coding for multi-view video. It generates side information by selectively and efficiently using both 3-dimensional warping based on the depth map with spatially adjacent frames and motion-compensated temporal interpolation with temporally adjacent frames. In this scheme the difference between the adjacent frames, the sizes of the motion vectors for the adjacent blocks, and the edge information are used as the selection criteria. From the experiments, it was observed that the quality of the side information generated by the proposed technique was improved by the average peak signal-to-noise ratio of 0.97dB than the one by motion-compensated temporal interpolation or 3-dimensional warping. The result from analyzing the rate-distortion curves revealed that the proposed scheme could reduce the bit-rate by 8.01% on average at the same peak signal-tonoise ratio value, compared to previous work.

Screen content like cartoons, captures of typical computer screens or video with text overlay or news ticker is an important category of video, which needs new techniques beyond the existing video coding techniques. In this paper, we analyze the characteristics of screen content and coding efficiency of HEVC on screen content. We propose a new coding scheme, which adopts a non-transform representation, separating screen content into color component and structure component. Based on the proposed representation, two coding modes are designed for screen content to exploit the directional correlation and non-translational changes in screen video sequences. The proposed scheme is then seamlessly incorporated into the HEVC structure and implemented into HEVC range extension reference software HM9.0. Experimental results show that the proposed scheme achieves up to 52.6% bitrate saving compared with HM9.0. On average, 35.1%, 29.2% and 23.6% bitrate saving are achieved with intra, random-access and low-delay configurations, respectively. The visual quality of the decoded video sequences is also significantly improved by reducing ringing artifacts around sharp edges and reserving the shape of text without blur.

Transform has been widely used to remove spatial redundancy of prediction residuals in the modern video coding standards. However, since the residual blocks exhibit diverse characteristics in a video sequence, conventional transform methods with fixed transform kernels may result in low efficiency. To tackle this problem, we propose a novel content adaptive transform framework for the H.264/AVC-based video coding. The proposed method utilizes pixel rearrangement to dynamically adjust the transform kernels to adapt to the video content. In addition, unlike the traditional adaptive transforms, the proposed method obtains the transform kernels from the reconstructed block, and hence it consumes only one logic indicator for each transform unit. Moreover, a spiral-scanning method is developed to reorder the transform coefficients for better entropy coding. Experimental results on the Key Technical Area (KTA) platform show that the proposed method can achieve an average bitrate reduction of about 7.95% and 7.0% under all-intra and low-delay configurations, respectively.

B-splines caught interest of many engineering applications due to their merits of being flexible and provide a large degree of differentiability and cost/quality trade-off relationship. However, they have less impact with continuous-time applications as they are constructed from piecewise polynomials. On the other hand, exponential spline polynomials (E-splines) represent the best smooth transition between continuous and discrete domains as they are made of exponential segments. In this paper, we present a complete analysis for an E-spline-based subband coding (wavelet) perfect reconstruction (PR) system. Derivations for the scaling and wavelet functions are presented, along with application of the proposed system in image compression and image denoising. In image compression, a comparison of the proposed technique compared with the B-spline-based PR system as well as the basic wavelet subband system with the SPIHT image codec is presented. In image denoising, we report the enhancement achieved with the proposed E-spline-based denoising approach compared with B-spline-based denoising and another basic denoising technique. In both applications, E-splines show superior performance as will be illustrated.

Visual features are successfully exploited in several applications (e.g., visual search, object recognition and tracking, etc.) due to their ability to efficiently represent image content. Several visual analysis tasks require features to be transmitted over a bandwidth-limited network, thus calling for coding techniques to reduce the required bit budget, while attaining a target level of efficiency. In this paper, we propose, for the first time, a coding architecture designed for local features (e.g., SIFT, SURF) extracted from video sequences. To achieve high coding efficiency, we exploit both spatial and temporal redundancy by means of intraframe and interframe coding modes. In addition, we propose a coding mode decision based on rate-distortion optimization. The proposed coding scheme can be conveniently adopted to implement the analyze-then-compress (ATC) paradigm in the context of visual sensor networks. That is, sets of visual features are extracted from video frames, encoded at remote nodes, and finally transmitted to a central controller that performs visual analysis. This is in contrast to the traditional compress-then-analyze (CTA) paradigm, in which video sequences acquired at a node are compressed and then sent to a central unit for further processing. In this paper, we compare these coding paradigms using metrics that are routinely adopted to evaluate the suitability of visual features in the context of content-based retrieval, object recognition, and tracking. Experimental results demonstrate that, thanks to the significant coding gains achieved by the proposed coding scheme, ATC outperforms CTA with respect to all evaluation metrics.

In this letter, multiple forward and inverse fast algorithm based transforms and their hardware-sharing design for 2 × 2, 4 × 4, and 8 × 8 transforms in H.264/AVC, and the 8 × 8 transform in audio video coding standard, 4 × 4 and 8 × 8 transforms in VC-1, and DCT/IDCT in MPEG-1/2/4 are developed with a low hardware cost for multistandard video coding applications. Compared with the directly combined fast transforms without shares, the proposed low-cost 1-D architecture reduces shifters by 67%, adders by 73%, and gate counts by 53.4%. The hardware-sharing efficiencies of shifters and adders in the proposed 1-D transform design are 32% and 25% more than those in the previous design, respectively. By 0.18-μm CMOS technology, the proposed 2-D transform architecture has less normalized power per mode and larger normalized hardware efficiency than the previous multiple-standard designs. The cost-effective 2-D full pipelined transform achieves multistandard real-time 1080HD at 60-Hz video encoding and decoding applications.

Video compression coding technology is the key to implement network video transmission, and the increasing requirements of network content transmission quality accelerate the development of video compression coding technology. H.264 is a highly compressed digital video codec standard, and is a hybrid coding mode of differential pulse code modulation (DPCM) and transform encoding. In this paper, we introduced several techniques of hybrid coding algorithm based on H.264 standard, including differential coding, block matching motion prediction method, normalization criteria, variable-size block motion compensation, and multiple hypothesis motion compensation. We did extensive experiments on variable-size block motion compensation coding method. The experimental results show that, with the premise of the 1/4 pixel motion compensation accuracy and hybrid coding, 8 × 8 or larger block partition of content for normal video object, can further make use of space correlation and improve compression efficiency.

Different from the previous video coding standards that employ fixed-size coding blocks (and macroblocks), the latest high efficiency video coding (HEVC) introduces a quadtree structure to represent variable-size coding blocks in the coding loop. The main objective of this study is to investigate a novel way to reuse these variable-size blocks to represent the foreground objects in the picture. Towards this end, this paper proposes three methods, i.e., flagging the blocks lying in the object regions flagging compression blocks (FCB), adding an object tree in each Coding Tree Unit to describe the objects' shape in it additional object tree (AOT) and confining the block splitting procedure to fit the object shape confining by shape (CBS). Among them, FCB and CBS add a flag bit in the syntax description of the block to indicate whether it lies in the objects region, while AOT adds a separate quadtree to represent the objects. For all these methods, the additional bits are then fed into the HEVC entropy coding module to compress. As such, the representation of visual objects in the pictures can be implemented in the HEVC coding loop by reusing the variable-size blocks and entropy coding, without additional coding tools. The experiments on six manually-segmented HEVC testing sequences (three in 1080P and three in 720P) demonstrate the feasibility and effectiveness of our proposal. To represent the objects in the 1080P testing sequences, the BD rate increases of FCB, AOT, and CBS over the HEVC anchor are 1.57%, 3.27%, and 5.93% respectively; while for the 720P conference videos, those are 4.57%, 17.23%, and 26.93% (note that the average bitrate of the anchor is only 1009 kb/s).

Recently, color plus depth map format 3-D video has been introduced as a new 3-D scene representation format, which gains the attention for its relatively low storage and transmission bandwidth requirement in comparison to the multiview video system. Usually, encoding of the color video and the depth data uses two traditional H.264 encoders. The complexity and hardware requirements to code one 2-D view and its associated depth data are nearly two times higher than coding one 2-D view. In this paper, we address the reduction of complexity of depth coding. Since both color videos and depth maps represent the same scene at the same time instant, it is likely for each macroblock to have similar coding information including the prediction mode and motion vectors. Taking advantage of correlations between coding information from depth maps and color videos, we propose a low complexity depth coding algorithm incorporating a fast motion search method and a fast mode decision method. Results show that the proposed algorithm reduces 80% computational complexity with a negligible loss of coding efficiency in comparison with the original Joint Multiview Video Coding encoder.

In region-of-interest (ROI)-based video coding, ROI parts of the frame are encoded with higher quality than non-ROI parts. At low bit rates, such encoding may produce attention-grabbing coding artifacts, which may draw viewer's attention away from ROI, thereby degrading visual quality. In this paper, we present a saliency-aware video compression method for ROI-based video coding. The proposed method aims at reducing salient coding artifacts in non-ROI parts of the frame in order to keep user's attention on ROI. Further, the method allows saliency to increase in high quality parts of the frame, and allows saliency to reduce in non-ROI parts. Experimental results indicate that the proposed method is able to improve visual quality of encoded video relative to conventional rate distortion optimized video coding, as well as two state-of-the art perceptual video coding methods.

In our work we present multiple description coding (MDC) scheme using contourlet transform (CT) for gray scale image. The principle is to prove performance of our algorithm in the coding field in contrast of wavelet that is widely used for image coding. The goal of our approach is to encode directional subbands resulting from the directional stage decomposition, to form the descriptions compressed and transmitted over different channels. The main objective of this work is to encode the source into four descriptions for transmission over packet loss channels, Most of multiple description scenarios are implemented in transform domain. A controlled amount of correlation is appended to the formed descriptions using a pairwise correlating transform (PCT). The experimental results of CT compared to discret wavelet transform (DWT) show that our method achieves better performance in term of delivered peak signal to noise ratio (PSNR) and visual image quality reconstruction.

A novel transform method providing a regionally structured sparse model of residue for a video coding is proposed. Conventional learning-based video coding methods consider only the number of transformed coefficients in a dictionary construction but disregard any structural form of residue. A structural form of coefficients is used for another factor, specified by a set of local block patterns, in a transform design so as to yield the regionally compact distribution of the coefficients. A transform coefficient coding method is also developed because the distribution of the coefficients is different with that of conventional transforms. It is demonstrated with experiments that the proposed method outperforms the coding efficiency and visual quality over high efficiency video coding/screen content coding reference software.

This paper provides an overview of the mode dependent coding tools in the development of video coding technology. In video coding, the prediction mode is closely related with the local image statistical characteristics. For instance, the angular intra mode contains the oriented structure information and the interpartition mode can reveal the local texture properties. Characterized by this fact, the prediction mode can be employed to facilitate the further encoding process, such as transform and coefficient coding. Recently, many mode dependent coding tools were proposed to provide more flexibility and thereby improve the compression performance. In general, these coding tools can be classified into three categories: mode dependent transform, residual reorder before transformation and coefficient scan after transformation. In this paper, these advanced coding algorithms are detailed and experiments are conducted to demonstrate their superior compression performance.

This paper describes an extension of the high efficiency video coding (HEVC) standard for coding of multi-view video and depth data. In addition to the known concept of disparity-compensated prediction, inter-view motion parameter, and inter-view residual prediction for coding of the dependent video views are developed and integrated. Furthermore, for depth coding, new intra coding modes, a modified motion compensation and motion vector coding as well as the concept of motion parameter inheritance are part of the HEVC extension. A novel encoder control uses view synthesis optimization, which guarantees that high quality intermediate views can be generated based on the decoded data. The bitstream format supports the extraction of partial bitstreams, so that conventional 2D video, stereo video, and the full multi-view video plus depth format can be decoded from a single bitstream. Objective and subjective results are presented, demonstrating that the proposed approach pro vides 50% bit rate savings in comparison with HEVC simulcast and 20% in comparison with a straightforward multi-view extension of HEVC without the newly developed coding tools.

With the development of modern signal processing technology, the method of Wavelet Transform (WT) has been greatly used in image and video processing. In this paper, a method to use Discrete Wavelet Transform (DWT) in intra-frame (I-frame) video compression is proposed for the purpose of better compression efficiency and quality on the basis of good visual effects. The method can be functioned as a preprocessing of I-frame coding. The experimental results show that the proposed method has an increase in both coding efficiency and quality.

With the advancement in the video compression and internet technology, the application of video streaming has increased. Because of different types of devices used for accessing the video application and the heterogeneous nature of the network the scalable video coding has become important in order to fulfill the needs of the user. Discrete wavelet transform (DWT) is best tool for scalable video coding because of its property of multiresolution. The coding and display order of pictures in H.264/MPEG4-AVC is completely decoupled and any picture can be marked as reference picture and is used for prediction of following picture independent of corresponding slice type. A wavelet based hierarchical coding structure has been proposed which selects best matched reference frame for the current frame to be encoded based on the mean square error (MSE) in the group of picture(GOP). The scalable encoding of hierarchical prediction has also been exploited. This algorithm has been compared with the performance of traditional multiresolution motion estimation technique based on peak signal to noise ratio. The proposed algorithm show an improvement of 13% in PSNR value over the traditional MRME technique.