Error concealment in JPEG2000 coded images

Nowadays data compression and authentication are believed to be vital to image transmission through heterogeneous infrastructure or storage at a centralized place. Though compression and authentication have independently grown to be matured technologies, but currently integration of these technologies is understood to be a key factor in handling tampering in images transmitted through unsecure channels like cloud. In this chapter, an error-resistant approach is investigated to add to low cost image authentication scheme to increase visual quality as well as improve author and user satisfaction. The image authentication includes content based digital signature that is watermarked and later diffused in the whole image before JPEG2000 coding. To tackle manipulations in the image, edge information of the image is examined to offset manipulations in the image transmission through noisy or open and unsecure channels. The edge image is sent along with JPEG2000 coded image to determine corrupted coefficients. The simulation results are conducted on test images for different values of bit error rate to judge confidence in error concealment within the received images.

Time-Scale Modification (TSM) is a well-researched field and allows for time-domain manipulation of a signal without modifying the pitch or timbre. Many TSM methods have been presented, however quantitative results on the quality of these methods are rare, with most methods reporting informal listening tests. This is likely due to the timecommitment and cost of subjective testing. Additionally, an objective measure of quality has not yet been developed that is suitable for timescaled signals. This dissertation describes the design of e ective objective measures of quality for TSM. TSM methods are, generally, single channel algorithms that give poor results when applied to multi-channel signals, as the phase relationship between channels must be maintained. This dissertation proposes a method and additional variant for maintaining the phase relationship between channels and retaining the presence in the centre of the stereo signal. The method involves pre- and post-processing the signal, with the variant processing each frame for real-time suitability. Sum and di erence transformations of the stereo signal are used for TSM and result in a large improvement in stereo phase coherence, consequently maintaining the stereo field. The proposed method produces a highquality stereo output and greatly improves quality over the independent channel processing method. It also allows for simple implementation around all existing TSM frameworks. A modification to the Epoch-Synchronous Overlap-Add (ESOLA) TSM algorithm is proposed in this dissertation. The proposed method, Fuzzy Epoch-Synchronous Overlap-Add, improves on the previous ESOLA method through cross-correlation of time-smeared epochs before overlap-adding. This reduces distortion and artefacts while the speaker's fundamental frequency is stable, as well as reducing artefacts during pitch modulation. The proposed method is tested against well-known TSM algorithms. It is preferred over ESOLA and gives similar performance to other TSM algorithms for voice signals. It is also shown that this algorithm can work effectively with solo instrument signals containing strong fundamental frequencies. No effective objective measure of quality for TSM exists. This dissertation details the creation, subjective evaluation and analysis of a dataset, for use in the development of an objective measure of quality for TSM. Comprising two parts, the training subset contains 88 source files processed using six TSM methods at 10 time-scales, while the testing subset contains 20 source files processed using three additional methods at four time-scales. The source material contains speech, solo harmonic and percussive instruments, sound effects and a range of music genres. 42,529 ratings were collected from 633 sessions using laboratory and remote collection methods. Analysis of results shows no correlation between age and quality of rating; equivalence between expert and non-expert listeners; negligible di erences between participants with and without hearing issues; and negligible di erences between testing modalities. Comparison of published objective measures and subjective scores shows the objective measures to be poor indicators of subjective quality. Initial results for a retrained objective measure of quality are presented with results approaching average loss and correlation values of subjective sessions. An objective measure of quality for time-scaled audio is proposed that makes use of the previously developed dataset and improves on reported results. The measure uses hand-crafted features and a fully connected network to predict subjective mean opinion scores. Basic and Advanced Perceptual Evaluation of Audio Quality features are used in addition to nine features specific to TSM artefacts. Six methods of alignment are explored, with interpolation of the reference magnitude spectrum to the length of the test magnitude spectrum giving the best performance. The proposed measure achieves an average Root Mean Squared Error (RMSE) of 0.490 and a mean Pearson Correlation Coe cient (PCC) of 0.864, equivalent to 97th and 82nd percentiles of subjective sessions respectively. The proposed measure is used to evaluate TSM algorithms, finding that Elastique gives the highest objective quality for solo instrument and voice signals, while the Identity Phase-Locking Phase Vocoder gives the highest objective quality for music signals and the best overall quality. Two single-ended objective quality measures for time-scaled audio are also proposed. These measure do not require a reference signal, nor alignment. Data driven features are created by either a convolutional neural network (CNN) or a bidirectional gated recurrent unit (BGRU) network, and are fed to a fully-connected network to predict subjective mean opinion scores. The proposed CNN and BGRU measures achieve an average RMSE of 0.608 and 0.576, and a mean PCC of 0.771 and 0.794, respectively. The proposed measures are used to evaluate TSM algorithms, and comparisons are provided for 16 TSM implementations. A literature review is included with required background knowledge. It includes the fundamentals of sound perception, sound capture, digital signal processing, time-scale modification methods used within research, and subjective and objective measures of quality. Full implementation of all proposed methods and measures can be found at github.com/zygurt/TSM, while the labelled dataset is available at http://ieee-dataport.org/1987.

Error concealment is a well-known technique to improve reproduced picture quality in cases which parts of the coded picture are not available at a decoder for reconstruction. The conventional error concealment methods generally make use of the correlation between a damaged marcoblock and its adjacent macroblocks in the same frame and/or the previous frame. However, performance results of concealment methods are different each other and dependent on how much damaged frames and macroblocks are correlated to surrounding ones temporally and spatially. Moreover, most of them can not conceal effectively the consecutively damaged frames or macro blocks caused by wireless bursty packet loss features, since motion vectors and correlation information between the damaged images are already destroyed. The proposed error concealment aware error resilient video coding is a new method which has an intelligent error concealment selector by using pre-estimation technique of the possibly damaged motion vectors at the encoder. The method recommends the best error concealment per macroblock by transmitting additionally its error concealment selection codes to the decoder. The chosen error concealment helps to conceal the damaged MB at the decoder even when losses of frames are bursty. The experimental results show the substantial effectiveness to improve video quality at the cost of a little bit overhead at the transmitted bit streams.

Visual attention guided image fusion with sparse representation

Image interpolation is used widely in the computer vision. Holding edge information is main problem in the image interpolation. By using bilinear and bicubic B-spline interpolation methods, a novel image interpolation approach was proposed in this paper. Firstly, inverse distance weighted average method was used to reduce image’s noise. Secondly, edge detection operator was used to extract image's edges information. It can help us to select different interpolation methods in the image interpolation process. Finally, we selected bilinear interpolation approach at non-edge regions, and bicubic B-spline interpolation method was used near edges regions. Further more, control vertexes were computed from pixels with calculation formula which has been simplified in the B-spline interpolation process. Experiments showed the interpolated image by the proposed method had good vision results for it could hold image's edge information effectively.

Optical CDMA systems have been investigated widely to apply for high speed LAN, because they allow multiple users to access the network simultaneously. For optical CDMA systems, both asynchronous and synchronous systems have been studied. With synchronous CDMA, the available number of signature codes is larger than that of asynchronous CDMA systems. For a given value of bit error rate, synchronous CDMA systems can accommodate more simultaneous users than asynchronous CDMA systems. Furthermore, synchronous CDMA can be efficiently used in conjunction with TDMA and WDMA on multimedia communication networks.This chapter discusses sequences, modulations, system architectures used in optical synchronous CDMA systems and their performance.

In this paper, a prescription-based error concealment (PEC) method is proposed. PEC relies on pre-analyses of the concealment error image (CEI) for I-frames and the optimal error concealment scheme for P-frames at encoder side. CEI is used to enhance the image quality at decoder side after error concealment (by spatial interpolation or zero motion) of the corrupted intra-coded MBs. A set of pre-selected error concealment methods is evaluated for each corrupted inter-coded MB to determine the optimal one for the decoder. Both the CEI and the scheme indices are considered as the prescriptions for decoder and transmitted along with the video bit stream based on a reversible data embedding technique. Experiments show that the proposed method is capable of achieving PSNR improvement of up to 1.48 dB, at a considerable bit-rate, when the packet loss rate is 20%

In spite of great advancements in multimedia data storage and communication technologies, compression of medical data remains challenging. This paper presents a novel compression method for the compression of medical images. The proposed method uses Ripplet transform to represent singularities along arbitrarily shaped curves and Set Partitioning in Hierarchical Trees encoder to encode the significant coefficients. The main objective of the proposed method is to provide high quality compressed images by representing images at different scales and directions and to achieve high compression ratio. Experimental results obtained on a set of medical images demonstrate that besides providing multiresolution and high directionality, the proposed method attains high Peak Signal to Noise Ratio and significant compression ratio as compared with conventional and state-of-art compression methods.

Traditional edge detectors are almost all based on the frame of Smoothing plus Differentiating plus Binarizing, some of which such as Canny can not provide rich description for edge information as well as can not make exact edge localization. In this paper, a new concept of General-Scale-Edge (GSE) is proposed and defined, and a novel frame for GSE extraction is constructed. Experimental results show that the proposed approach can exactly extract edges at various scales as well as provide rich forms for the description of edge information in images.

In this paper, we present a novel image denoising algorithm, specifically designed to effectively restore both the edges and texture of images. This is achieved through the use of an innovative model known as the overlapping group sparse fractional-order total variation regularization model (OGS-FOTVR). The OGS-FOTVR model ingeniously combines the benefits of the fractional-order (FO) variation domain with an overlapping group sparsity measure, which acts as its regularization component. This is further enhanced by the inclusion of the well-established L2-norm, which serves as the fidelity term. To simplify the model, we employ the alternating direction method of multipliers (ADMM), which breaks down the model into a series of more manageable sub-problems. Each of these sub-problems can then be addressed individually. However, the sub-problem involving the overlapping group sparse FO regularization presents a high level of complexity. To address this, we construct an alternative function for this sub-problem, utilizing the mean inequality principle. Subsequently, we employ the majorize-minimization (MM) algorithm to solve it. Empirical results strongly support the effectiveness of the OGS-FOTVR model, demonstrating its ability to accurately recover texture and edge information in images. Notably, the model performs better than several advanced variational alternatives, as indicated by superior performance metrics across three image datasets, PSNR, and SSIM.

Inspired by the human vision system, neuromorphic vision systems simulating the mechanism of signal processing in retina are extensively investigated. This paper describes a silicon neuronal network with bio-inspired structure detects the edge information in image. The digital spiking silicon neuron (DSSN) and silicon synapse models are used to reproduce the neuron, synapse and their dynamic behaviors. The biological receptive fields are realized by the combination of excitatory and inhibitory synapses. Our silicon neuronal network is optimized for the hardware implementation. It is expected to have low hardware consumption and high running speed. The simulation results show that edge detection is successful in our network.

With the rapid development of technology, artificial intelligence is gradually applied in the field of image processing. Traditional image processing and recognition takes human extraction of features as a means to describe and characterize low-level edge information in images, but a large number of features extraction causes certain troubles to image recognition, and it is difficult to extract deep-level information. In recent years, deep learning, as an important emerging technology in the field of image recognition, has broad application prospects, and the research on image recognition technology has important theoretical value and practical significance to promote the development of computer vision and artificial intelligence. It opens up a whole new direction in image processing research, thus meeting the demand for efficient image processing. This paper reviews the applications of deep learning in image recognition, analyzes the classical convolutional neural network model, and summarizes the applications of deep learning in the fields of license plate recognition, face recognition, and medical image recognition.

The growth of modern wireless communications has increased the demand for high quality data services. This article presents an analysis of the performance of a DVB-S2 system with MIMO technology. A method for using a receiver-transmitter based on DVB-S2 with MIMO technology, provides a better quality of service for data transmission and discusses ways to improve the quality of data transmission using size and type converters of transmitting and receiving data, an amplifier and a normalizer Gain. The circuit was examined for bit error rate (BER) and peak signal-to-noise ratio (PSNR) between AWGN and multipath Rayleigh fading images operating under intersymbol interference (ISI). The analysis was carried out and a graph of the influence of ISI was obtained. The proposed system has the best quality of service for data transmission for fading channels. The transmission of images in the system includes a Rayleigh channel and AWGN with different values of the signal-to-noise ratio (SNR, dB) and different values of Gain. The Gain values change the BER and PSNR values (7.834 dB, 8.385 dB, and inf) at an SNR of -58 dB with respect to Gain values of 1, 32400 and 648000, respectively. Also obtained eye diagrams, where you can see a decrease in the negative impact of intersymbol interference with an increase in the value of Gain..This system can be used to wirelessly transmit higher quality, high-resolution radiological images (such as computed tomography or magnetic resonance imaging) over a wireless network to remote healthcare workers.

This paper presents an efficient error concealment method for multiview video plus depth (MVD) delivery over error-prone channels based on the view synthesis information. Specifically, we propose an enhanced temporal error concealment (TEC) method by estimating the probable partition and missing MVs of a corrupted macroblock (MB) using the synthesized information. Furthermore, the view synthesis error concealment (VSEC) approach that directly uses the synthesized pixels to conceal the corrupted MBs is also adaptively utilized together with the TEC approach. Experimental results have shown the effectiveness of the proposed method compared with the existing methods in terms of both objective and subjective measures.

To evaluate the vocal outcomes of patients with early-stage glottic carcinoma undergoing laser resection with adjuvant cryoablative therapy. Retrospective review. Tertiary care center. Patients Twenty patients with early-stage glottic carcinoma. Intervention Treatment of early-stage glottic carcinoma with endoscopic carbon dioxide laser resection in conjunction with cryoablation. Disease-free survival and subjective and objective measures of posttreatment voice quality, based on serial videolaryngostroboscopy. There was 1 local treatment failure, with an overall mean disease-free follow-up of 32.6 months (range, 3-93 months). Carbon dioxide laser resection and cryoablative therapy were associated with a significant improvement in subjective voice quality (P<.001). Long-term dysphonia was uniformly improved vis-à-vis the pretreatment condition, even among patients with the most advanced disease undergoing the widest resections. Posttreatment web formation was not noted among 4 patients with anterior commissure involvement. Endoscopic laser laryngeal surgery performed in conjunction with cryotherapy for early-stage glottic carcinoma yielded excellent primary site control, while improving subjective and objective measures of voice quality. Combined laser surgery and cryotherapy is a possible alternative to radiotherapy for selected patients with early-stage glottic carcinoma who desire curative therapy, while optimizing vocal outcomes.