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The classical chroma subsampling involves downsampling chrominance components (U and V) while maintaining the luminance component (Y). Recently, a study has attempted to change Y for the chroma-subsampling process, and improved results in the reconstructed image quality are obtained. However, computational complexity remains an issue because the study examines all the candidates in the determined range for minimal pixel distortion for the modified Y; the complexity can be high if the range is large. In this study, we reduced the candidate number to seven at most, which is mathematically optimized. Boundary points for in-the-range red, green, and blue (RGB) values are first decided, followed by the determination of the intervals that concatenate the entire curve (at most seven intervals). Each interval belongs to one of the seven sub-cases of different linear combinations of individual curves. The optimal solution (modified Y) for each sub-case is derived, which is in highly efficient form. Compared with the existing method, the proposed fast method ensures that the image quality is preserved, while the number of search candidates is reduced by 61.30%–69.19% on average, and the computational time of the process is reduced by 31.39%–58.97%, thereby demonstrating an efficient performance.

Dynamic point clouds (DPC) are new media storage formats that allow end-users to watch objects/scenes in a three-dimensional (3D) sense. It can be displayed from different angles throughout time. However, the raw size of a point cloud is huge because there can be millions of points (each containing color triplet and location triplet information) in a point cloud, and there can be multiple point clouds in a DPC. Video-based point cloud compression (V-PCC) is developed to project a 3D point cloud to 2D images: attribute, geometry, and occupancy images. After padding, the 2D images are compressed using the well-established high-efficiency video coding (HEVC). In this study, we first employ an occupancy image to propose a blocky occupancy flag (BOF), to denote the occupancy information on “a block basis”. For coding attribute and geometry images, we use a BOF to develop a fast coding unit (CU) algorithm for early termination of the CU search recursion. We also utilize the geometry images to calculate the 2D and 3D information of each pixel, for 2D/3D spatial homogeneity of the pixels to design fast CU decision. In addition, we proposed a modified rate-distortion optimization for different color components considering the picture order count (POC) structure in HEVC/V-PCC. Finally, we propose an HEVC input pixel modification method based on a BOF to reduce the unnecessary information to be coded for attribute images. Compared with the state-of-the-art fast V-PCC encoding method, the proposed work outperforms by up to 2.31% in Bjøntegaard delta bit rates (BDBR) (with very slight loss by only up to 0.38%), and improves the time saving performances by up to 7.84% for two different testing datasets.

This paper proposes a new compression algorithm based on color sampling and Block Truncation Coding (BTC). It is composed of several steps, color sampling, BTC parameter training, threshold selection, sub-sampling, prediction and quantization, and Huffman coding. The color sampling was applied using YEF color space which reduced 34% storage. The subsequent sub-sampling, quantization table prediction, and Huffman coding further increase compression rates. In addition, the BTC parameter training step finds the best reconstruction value for each 4*4 block and bitmap threshold value to improve the image quality. Compared with previously studies, the proposed algorithm shows a better average compression rate.

In this paper, a hardware-oriented lossless EEG compression algorithm including a two-stage prediction, voting prediction and tri-entropy coding is proposed. In two stages prediction, 27 conditions and 6 functions are used to decide how to predict the current data from previous data. Then, voting prediction finds optimal function according to 27 conditions for best function to produce best Error (the difference of predicted data and current data). Moreover, a tri-entropy coding technique is developed based on normal distribution. The two-stage Huffman coding and Golomb-Rice coding was used to generate the binary code of Error value. In CHB-MIT Scalp EEG Database, the novel EEG compression algorithm achieves average compression rate to 2.37. The proposed hardware-oriented algorithm is suitable for VLSI implementation due to its low complexity.

With the rapid development of wireless communication systems and various emerging applications, software implementations could not satisfy the real-time applications of location-based services (LBS). This article presents a three-dimensional (3D) localization algorithms based on positioning and tracking approaches. The proposed real-time positioning and tracking approaches are implemented with field programmable gate arrays (FPGAs). The hardware implementation of the FPGA-based algorithms can improve the traditional matrix operation approaches, and it has advantages of high portability, low cost, and low executing time for LBS applications.

This paper presents a hardware-oriented image stitching algorithm with characteristic of low complexity. The proposed algorithm is developed by using the feature from accelerated segment test (FAST) algorithm to extract feature point, and combines scale invariant feature transform (SIFT) descriptor to perform feature matching. In order to be suitable for hardware implementation, the proposed algorithm was simplified by using the optimal seam algorithm widely used to achieve seamless image stitching and eliminate ghosting. Moreover, the obvious lines at the stitching seam due to the color difference are reduced by the low-pass filter. Experiment results show that the proposed algorithm has benefits of seamless images with high quality and calculation with low complexity.

In this paper, we present a novel 3D structure-awareness RGB-D video compression scheme, which applies the proposed 3D super-point clustering to partition the super-points in a colored point cloud, generated from an RGB-D image, into a centroid and a non-centroid super-point datasets. A super-point is a set of 3D points which are characterized with similar feature vectors. Input an RGB-D frame to the proposed scheme, the camera parameters are first used to generate a colored point cloud, which is segmented into multiple super-points using our multiple principal plane analysis (MPPA). These super-points are then grouped into multiple clusters, each of them characterized by a centroid super-point. Next, the median feature vectors of super-points are represented by the K singular value decomposition (K-SVD) based sparse codes. Given a super-point cluster, the sparse codes of the median feature vectors are very similar and thus the redundant information among them are easy to remove by the successive entropy coding. For each super-point, the residual super-point is computed by subtracting the feature vectors inside from the reconstructed median feature vector. These residual feature vectors are also collected and coded using the K-SVD based sparse coding to enhance the quality of the compressed point cloud. This process results in a multiple description coding scheme for 3D point cloud compression. Finally, the compressed point cloud is projected to the 2D image space to obtain the compressed RGB-D image. Experiments demonstrate the effectiveness of our approach which attains better performance than the current state-of-the-art point cloud compression methods.

Due to the rapid development of wireless communication applications, software implementations cannot fulfill real-time computing to satisfy the in-time requirement of location-based services (LBS) applications. This article presents a location-estimation technique based on positioning and tracking algorithms, and the high-performance algorithms are implemented in field programmable gate array (FPGA) approaches, which have the features of pipeline structure and real-time processing. In terms of the FPGA approaches, our results demonstrate that the performance of location accuracy of the proposed approaches is almost the same as those of the software implementations, and it has the advantage of processing time.

In recent years, wireless communication applications have been widely used in various industries, such as the popularity of smart phones, large-scale cloud computing platforms, and the Internet of Things (IoT) in smart cities. Positioning techniques have made great progress in terms of positioning accuracy and usability. These approaches gradually infiltrate all aspects of social life from the areas of navigation, aerospace, aviation, military, and natural disaster prevention. Namely, these techniques become an indispensable and important applications in daily life, such as personnel search, location search, traffic management, vehicle navigation, and route planning. Therefore, an adaptive low-complexity location-estimation approach combined alpha-beta (α-β) filtering algorithm with the Kalman filtering (KF) algorithm is proposed and implemented in this article. The proposed tracking approach is based on the α-β filtering algorithm extracted the coefficients of the Kalman gain from the KF algorithm. In addition, in order to enhance the performance and calculating speed, we propose the hardware implementation for KF algorithm and α-β filtering algorithm. The proposed concept is performed by field programmable gate array (FPGA), which has the features of pipeline structure and real-time processing. Under a stationary environment, as compared with the software implementation for location tracking, the proposed FPGA training, decision, and tracking approach not only can achieve the location accuracy close to the KF tracking approach but has much lower computational complexity and provides better performance than that of non-tracking approaches.

For existing methods on YUV420 subsampling from YUV444 image format, the Y is constrained to change the UV values, or the UVs are constrained to change the Y value. We proposed a method of chroma subsampling with changing YUV values at the same time for YUV420 images. We proposed an assumption (observation) to reduce the algorithm complexity. The experimental results show that the proposed work is better than existing state-of-the-art methods by 1.64 dB and 0.61 dB on average in CPSNR (Composite Peak Signal-to-noise Ratio).

In this paper, a new color image compression algorithm based on Absolute Moment Block Truncation Coding (AMBTC) and entropy coding is proposed. The AMBTC improves the compression performance of Block Truncation Coding (BTC) obviously. A novel technique by using eight different types of bitmap tables is developed for the proposed algorithm. In addition, an entropy coding technique combining prediction and Huffman Coding approach is included in the proposed algorithm and decreased the data amount of luminance images. The experimental results show that more than 82% data amount can be compressed by using the eight different types of novel bitmap tables. Compared with previous studies, this work has better compression performance than previous 4*4 BTC, JPEG, and JPEG-LS algorithms.

In this paper, an optimized backlight power saving algorithm using joint power-peak signal-to-noise ratio (PSNR) characteristics among multiple frames is proposed. In existing state-of-the-art methods, the optimal decision for clipping point is made only with consideration of current frame, and the target frame PSNR are set the same for all frames. Therefore they have the same power-PSNR performance. In the proposed work, we show that better power-PSNR performance can be achieved by assigning different target frame PSNR for different frames. In the proposed algorithm, the decisions for clipping points associated with all frames are made at once. The inefficiency ratio of each clipping point is evaluated and put into the algorithm design. An optimization problem is formulated, and a solution-search procedure is proposed. Compared with state-of-the-art methods, the proposed work performs better in both aspects of power and PSNR; the BD-PSNR and the BD-Power of the proposed work against all other methods are better for every tested video; the BD-PSNR and the BD-Power can be as high as 19.80 dB on average, and the BD-Power can be as low as – 26.25% on average.

This paper presents a novel motion vector recovery and error concealment algorithm with the utilization of encoding partition information for H.264/AVC. The motion vectors for each missing pixel location are derived using available neighboring pixel motion vectors, which contribute to the generation of the missing motion vectors inversely proportional to the distance between them. The motion extrapolation method is used to project the encoding partition information from the reference frame into the current frame with different levels of overlapping of lost pixels. The different levels of overlapping can help determine the estimated encoding partition information in the lost macroblock (MB). Finally, the pixels that are determined to be of the same estimated partition share the same motion vector in order to maintain the integrity of the estimated moving objects in the lost MB. This proposed pixel-based motion vector with partition (PMVP) method compares with the state-of-the-art Zhou's method, Lin's method, and Lie's method. For total average in packet loss rates of 3%, 7%, 16%, and 20%, PMVP is better than Zhou by 0.88, 1.02, 1.05, and 1.01 dB, respectively; Lin by 0.22, 0.32, 0.35, and 0.33 dB, respectively; and Lie by 4.12, 4.98, 4.15, and 3.88 dB, respectively. Therefore, the proposed PMVP performs the best on average among all the methods.

In this paper, we proposed a backlight dimming algorithm for videos, aiming to achieve better video quality measured in video quality metric (VQM) with controlled power consumption. In order to avoid the complex computation of VQM in the testing process, a training procedure is performed to find prediction model. For each training video, a VQM result curve corresponding to different clipping points is first built, to facilitate the consequent search for optimal clipping point. To build a prediction model for optimal clipping point to be used in testing stage, the optimal clipping point of each video is associated with the spatial information (SI) and temporal information (TI) of the video; the association is modeled by a 2-D LOWESS (LOcally WEighted Scatterplot Smoothing) surface. Therefore in the testing phase, for every video, the SI and TI are first computed, and mapped into the LOWESS model to find the predicted optimal clipping point, which is applied to all frames in the video. A generalized version of the proposed method is designed to have even better power reduction performance. The experiment results show that the generalized proposed work can achieve the best power reduction (17.9%), compared with state-of-the-art methods I2GEC (10.5%), MGEC4 (11.2%), MGEC16 (8.7%), and SPBD (8.7%). And the video quality of the generalized proposed method can be maintained, showing almost no visual difference with the original video. Thus, it is demonstrated that the proposed work can achieve good balance between video quality and power consumption.

We propose a fast algorithm with both CU size and PU mode decision method for HEVC.All of the 13 neighboring CTUs are analyzed to predict the current CU depth.k-means method is used to classify the neighboring CTUs into different groups.PU mode candidates are selected according to the CU depth.The proposed method gets 56.71% and 59.76% time reduction with LP and RA profiles. High Efficiency Video Coding (HEVC) adopts complex quadtree-structured CU (coding unit) and PU (prediction unit) modes. Thus, the search process for optimal modes causes high computational complexity in HEVC. To solve this problem, a fast coding algorithm is proposed. Thirteen neighboring CTUs (Coding Tree Unit) are divided into three classes with the k-means method, and are selectively used as the reference CTUs to calculate the predicted CU depth levels for the current CTU. Additionally, for every CU depth, we skip some rarely used PU modes to reduce the complexity of the PU selection algorithm. Experimental results show that compared with the HEVC reference software, our algorithm can achieve 56.71% time saving with Low Delay Configuration profile and 59.76% with Random Access Configuration profile, whereas values of BDBR (Bjontegaard Delta Bit Rate) are only 1.0517% and 0.9918%, respectively. Compared with five state-of-the-art algorithms, the proposed algorithm has significant encoding time reduction with good bitrate performances.

High-efficiency video coding (HEVC) utilizes a quadtree-structured coding unit (CU) for more possible sizes of basic blocks for later prediction. The exhaustive search for the optimal CU depth requires high complexity, which causes difficulties for real-time streaming. In the proposed method, we make use of the residual statistics of prediction units (PUs) in the current encoded CU to make a decision on CU early termination. The residual histogram is discussed, and the optimal early termination (ET) zone is designed; if the PU residual statistics fall in the ET zone, the early termination is triggered. The optimal parameters for the ET zone are solved and analyzed in the training stage, and can be estimated with table lookup and simple computations in the testing stage. Another early termination method using the neighboring SKIP information is also proposed. We use SKIP information from more meaningful CU neighbors defined and used in HEVC. We combine the proposed residual-based and SKIP-based algorithms with a state-of-the-art method. The experimental results show that compared to two state-of-the-art methods in both low delay (LP) and random access (RA) configurations, our preferred proposed combination can achieve the best BDBR results (0.737 % in LP and 0.471 % in RA) with a good corresponding TS performances (49.87 % in LP and 54.96 % in RA), making HEVC more suitable for real-time applications.

We propose a fast coding unit (CU) depth decision algorithm in the High Efficiency Video Coding (HEVC) procedure based on statistical analysis. First, we derive a set of optimized weights of surrounding CU decisions to predict the current CU decision for 3 different Largest Coding Unit (LCU) classes. Second, for a given predicted current CU decision, we analyze the possible true current CU decisions, aiming to find the correspondence. A corresponding table is found and can be used to achieve target prediction accuracy. Third, for early termination of the encoding processes, the 3 early termination methods in a state-of-the-art work, as well as their different combinations, are evaluated. We show that using one of them is sufficient for saving time while encoding to keep the implementation complexity low. Compared with full CU search in HEVC standards, the proposed method reduces the encoding time by 57 and 49 % on average with Low Delay and Random Access profiles, respectively, with acceptable bitrate and PSNR performances. Compared with two state-of-the-art methods, the encoding time reduction is up to 23 and 13 % with Low Delay profile, 7 and 3 % with Random Access profile, on average, whereas the performances of bitrate and PSNR are similar.

This article presents location-estimation algorithm based on tracking algorithm for indoor dynamic environments. Wireless positioning techniques have gained more popularity in recent years. There are many approaches for indoor positioning systems, such as received signal strength (RSS), angle of arrival, time of arrival, and time difference of arrival. In terms of indoor positioning approaches by trilateration technique to determine the location of mobile devices, this article illustrates the concept of the measured range extract from time of arrival approaches. Then an easy geometric technique is applied. Furthermore, in order to enhance performance, Kalman filtering approach is used to track and correct the movement path of mobile device to effectively improve positioning accuracy.

Recently, intelligent automobile electronics, advanced driver assistance system (ADAS) and intelligent transport system (ITS) have become to make great strides towards to the goal of automatic driving and smart cities. Therefore, how to enhance the visibility no matter at night or excessive exposure turns into an important issue of the research. In this paper, a novel effective contrast enhancement and hardware-oriented algorithm is proposed. The proposed contrast enhancement algorithm includes a RGB to HSV module, a mirror expansion module, a weighted filter and a Gamma correction module. A new technique called weighted filter was used to pick up the best brightness factor for the proposed algorithm. To be suitable for VLSI implementation, the proposed algorithm was developed based on low-complexity and low-memory-requirement. Compared with previous studies, this work proposed a high quality and low complexity hardware-oriented contrast enhancement algorithm for VLSI implementation.

A large number of demosaicking methods have been proposed. However, the algorithms get more complicated. In this paper, we proposed an efficient demosaicking filter using modified conversion matrix and the CFA (color filter arrays) pixels. The CFA image is first directly expanded into 3 full layers of R, G and B, where the missing values for each color are set as unknown numbers. The missing values are then produced by the proposed 3×3 filter to obtain temporary full RGB layers. The temporary full RGB layers are then used to produce Cb and Cr layers. To improve the image quality, the Cb and Cr layers are further filtered by the bilateral filter. To obtain the final full RGB layers, based on the standard YCbCr-to-RGB matrix conversion, we proposed to use a novel CfCbCr-to-RGB matrix conversion. For each location in an image, the Cf is the available CFA pixel value, which can be R, G or B, therefore there are 3 corresponding conversion matrix. Compared with the state-of-the-art methods using the Kodak dataset, the average improvement CPSNR gains are 2.22dB, 2.03dB and 1.54 dB.

The Joint Video Exploration Team (JVET) has established the latest video compression standard, Future Video Coding (FVC). However, quadtree plus binary tree (QTBT) based coding unit (CU) structure increases noticeable computational complexity in FVC intra coding. In this paper, a fast intra CU binary tree partition decision algorithm based on spatial features is proposed. The proposed method compared to JEM5.0 can save 23% encoding time with only 0.55% BDBR increment.

In this paper, an application of sparse optimization in the error concealment area is proposed. The spatial and temporal formulations of the pixels in the current frame and reference frame are proposed to solve the problem. Based on the sparse characteristics of nature images, we form sparse optimization problems for both formulations. The optimization problem is solved by the primal-dual interior point method. The solutions are combined for better results. By solving for a limited numbers of significant predictors using the sparse optimization, our algorithm performs subjectively and objectively better for the concealed result; compared to two state-of-the-art spatial-temporal hybrid error concealment methods, the proposed methods can improve by up to 0.19 dB and 1.12 dB in PSNR (Peak Signal-to-Noise Ratio).

The proposed paper discusses two switching error concealment algorithms: SECA (Switching Error Concealment Algorithm) and OSECA (Optimal Switching Error Concealment Algorithm) to efficiently switch among three existing error concealment methods for optimal performance and complexity tradeoff. SECA uses the motion vector statistics in the neighborhood of the lost MB (Macro Block) to make the switching decision. SECA is better than the best-PSNR method by 0.14 dB in PSNR and better than the best-win-rate method by 1.49 % in win rate, with 63 % time complexity reduction. OSECA further considers the correlation between surrounding pixels of the lost MB and the pixels in the reference frame. An optimization problem is formulated to derive the optimal decision thresholds. For OSECA, the PSNR performance is further improved by 0.24 dB from SECA and the time complexity reduction from the existing work can increase to 69 %. On different testing sets of the lost MBs, the proposed SECA and OSECA perform consistently against existing methods. Compared to SECA in the testing sets, OSECA has larger PSNR improvement (by up to 0.23 dB) against the best-PSNR method, and has larger time complexity reduction (by up to 71 %) against existing method.

Motion-vector-based error concealment algorithms recover lost blocks by motion compensation with the estimated motion vectors. Traditionally, the estimated motion vectors for the lost block are derived by the direct usage of the neighboring available motion vectors. In this work, the residual signal in a block encoded in the video bitstream is used to measure the reliability of the corresponding motion vector. The residual information is applied to weight the neighboring available motion vectors differently, to further improve a newly proposed mathematical formulation between encoded motion vectors and extrapolated motion vectors. The proposed error concealment method using the residual information outperforms a state-of-the-art error concealment algorithm by up to 2.12 dB in sequence PSNR, and outperforms a classic DMVE method by up to 8.27 dB.

In this paper, we proposed an adaptive weighting for joint source and channel rate-distortion optimization. The weighting is adjusted based on some measurements of MSE in previous encoded MB. The resulting videos for source loss and channel loss perform better than our prior work; the proposed method has higher winning rate over H.264 standard with the rate being 93.66%. Another novelty is that the proposed method does not require prior knowledge about the performance of different weightings, and the optimal weighting can be found adaptively.

Recently video communication has been massively adopted in internet transmission, and H.264/AVC is the major standard for the compressed video stream. To overcome channel errors resulted from the unstable connection quality of internet, many methods have been proposed for error concealment. Motion vector (MV) recovery is one of the most commonly used methods. We proposed a novel MV recovery method that utilizes the encoding residual information in the neighborhood of the lost MBs, to enhance the performance of MV extrapolation (MVE) and better conceal errors. In our proposed method, better MVs could be found after interpolation with inverse residual weighting. Experimental results show that compared to the state-of-the-art error concealment algorithm, our algorithm provides better performance subjectively and objectively.

For videos transmitted in an error-prone network, it is necessary to protect the source bitstream. Based on our packet loss visibility model, we minimize the end-to-end video quality degradation when transmitted in an AWGN channel using Rate-Compatible Punctured Convolutional codes for a given channel rate budget. We transform the original problem into a binary-decision problem, then we solve this integer program by the Branch and Bound method. Experimental results show that our visibility-based unequal error protection can improve the received video quality compared to equal error protection.

In error-prone channels, forward error correction is necessary for protecting important data. In this paper, we use a packet loss visibility model to evaluate the visual importance of video packets to be transmitted. With the loss visibility of each packet, we use the Branch and Bound method to optimally allocate rates of Rate-Compatible Punctured Convolutional codes. The complexity of our prior algorithm can be reduced by k-means clustering before using the Branch and Bound method. Experimental results show that the proposed unequal error protection algorithm can improve upon the received video quality compared to our prior work with much lower complexity.