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In this paper, a simple image compression algorithm is proposed for wireless capsule endoscopy. The proposed algorithm consists of new simplified YUV colour space, corner clipping, uniform quantization, subsampling, differential pulse code modulation and Golomb Rice code. Simplified YUV colour space is proposed based on special nature of endoscopic images and provide good results. The quantization and subsampling are used as lossy compression techniques and fixed Golomb-Rice code is used to encode residual value obtained after differential pulse code modulation operation. Here performance of different combination of quantization and subsampling techniques are analyzed based combination along with the proposed compression algorithm provides compression ratio of 89.3% and peak signal noise ratio of 45.1. the proposed algorithm provided better results as compared to various reported algorithms in literature in term of CR and PSNR.

In a computing environment, higher resolutions generally require more memory bandwidth, which inevitably leads to the consumption more power. This may become critical for the overall performance of mobile devices and graphic processor units with increased amounts of memory access and memory bandwidth. This paper proposes a lossless compression algorithm with a multiple differential pulse-code modulation variable sign code Golomb-Rice to reduce the memory bandwidth requirement. The efficiency of the proposed multiple differential pulse-code modulation is enhanced by selecting the optimal differential pulse code modulation mode. The experimental results show compression ratio of 1.99 for high-efficiency video coding image sequences and that the proposed lossless compression hardware can reduce the bus bandwidth requirement.

Compressive Video Sensing (CVS) is the video coding framework that uses compressive sensing (CS) theory into video codec to reduce the burden of encoding. Various existing methods are developed for CVS, but enhancing reconstruction speed and reconstruction quality remains a great challenge in video compression. Hence, an optimal method is developed using the proposed Differential Pulse Code Modulation+Conditional Autoregressive-Salp Swarm (DPCM+CA-SS) model for CVS system by considering the characteristics of CVS frame. The spatial redundancy of video frames is explored by partitioning the video frames into several blocks such that each block is measured using the measurement matrix. The measurement vectors are quantized using DPCM. The key frames are compressed into bits using Huffman coding, and the packet is transmitted to the decoder side. The temporal redundancy of CVS model is explored by encoding the frames using CA-SS-based prediction model to achieve Motion Aligned (MA) optimal reconstruction. However, the proposed CA-SS is designed by the integration of Conditional Autoregressive Value at Risk (CAViaR) and Salp Swarm Algorithm (SSA), respectively. However, the proposed DPCM+CA-SS obtained maximum PSNR and SSIM of 39.6649 db and 0.9120, and the minimum total time of 4.9523 s, respectively.

A compression method based on non-uniform binary scalar quantization, designed for the memoryless Laplacian source with zero-mean and unit variance, is analyzed in this paper. Two quantizer design approaches are presented that investigate the effect of clipping with the aim of reducing the quantization noise, where the minimal mean-squared error distortion is used to determine the optimal clipping factor. A detailed comparison of both models is provided, and the performance evaluation in a wide dynamic range of input data variances is also performed. The observed binary scalar quantization models are applied in standard signal processing tasks, such as speech and image quantization, but also to quantization of neural network parameters. The motivation behind the binary quantization of neural network weights is the model compression by a factor of 32, which is crucial for implementation in mobile or embedded devices with limited memory and processing power. The experimental results follow well the theoretical models, confirming their applicability in real-world applications.

Binaural sound coding strategies can improve speech intelligibility for cochlear implant (CI) users. These require a signal transmission between two CIs. As power consumption needs to be kept low in CIs, efficient coding or bit-rate reduction of the signals is necessary. In this work, it is proposed to code the electrical signals or excitation patterns (EP) of the CI instead of the audio signals captured by the microphones. For this purpose we designed a differential pulse code modulation based codec with zero algorithmic delay to code the EP of the advanced combination encoder (ACE) sound coding strategy for CIs. Our EP codec was compared to the G.722 64 kbit/s audio codec using the signal-to-noise ratio (SNR) as objective measure of quality. On two audio-sets the mean SNR was 0.5 to 13.9 dB higher when coding the EP with the proposed coding method while achieving a mean bit-rate between 34.1 and 40.3 kbit/s.

Compression of images is of great interest in applications where efficiency with respect to data storage or transmission bandwidth is sought.The rapid growth of social media and digital networks have given rise to huge amount of image data being accessed and exchanged daily. However, the larger the image size, the longer it takes to transmit and archive. In other words, high quality images require huge amount of transmission bandwidth and storage space. Suitable image compression can help in reducing the image size and improving transmission speed. Lossless image compression is especially crucial in fields such as remote sensing healthcare network, security and military applications as the quality of images needs to be maintained to avoid any errors during analysis or diagnosis. In this paper, a hybrid prediction lossless image compression algorithm is proposed to address these issues. The algorithm is achieved by combining predictive Differential Pulse Code Modulation (DPCM) and Integer Wavelet Transform (IWT). Entropy and compression ratio calculation are used to analyze the performance of the designed coding. The analysis shows that the best hybrid predictive algorithm is the sequence of DPCM-IWT-Huffman which has bits sizes reduced by 36%, 48%, 34% and 13% for tested images of Lena, Cameraman, Pepper and Baboon, respectively.

In this paper, we introduce the zero-delay multiple-description problem, where an encoder constructs two descriptions and the decoders receive a subset of these descriptions. The encoder and decoders are causal and operate under the restriction of zero delay, which implies that at each time instance, the encoder must generate codewords that can be decoded by the decoders using only the current and past codewords. For the case of discrete-time stationary scalar Gauss&mdash, Markov sources and quadratic distortion constraints, we present information-theoretic lower bounds on the average sum-rate in terms of the directed and mutual information rate between the source and the decoder reproductions. Furthermore, we show that the optimum test channel is in this case Gaussian, and it can be realized by a feedback coding scheme that utilizes prediction and correlated Gaussian noises. Operational achievable results are considered in the high-rate scenario using a simple differential pulse code modulation scheme with staggered quantizers. Using this scheme, we achieve operational rates within 0 . 415 bits / sample / description of the theoretical lower bounds for varying description rates.

In this paper, a simple color image compression system has been proposed using image signal decomposition. Where, the RGB image color band is converted to the less correlated YUV color model and the pixel value (magnitude) in each band is decomposed into 2-values; most and least significant. According to the importance of the most significant value (MSV) that influenced by any simply modification happened, an adaptive lossless image compression system is proposed using bit plane (BP) slicing, delta pulse code modulation (Delta PCM), adaptive quadtree (QT) partitioning followed by an adaptive shift encoder. On the other hand, a lossy compression system is introduced to handle the least significant value (LSV), it is based on an adaptive, error bounded coding system, and it uses the DCT compression scheme. The performance of the developed compression system was analyzed and compared with those attained from the universal standard JPEG, and the results of applying the proposed system indicated its performance is comparable or better than that of the JPEG standards.

A fixed rate speech coder based on the filter bank and the non-uniform sampling technique is proposed. The non-uniform sampling is achieved by the detection of inflection points (IPs). A speech block is band passed by the filter bank, and the subband signals are processed by the IP detector, and the detected IP patterns are compared with entries of the IP database. For each subband signal, the address of the closest member of the database and the energy of the IP pattern are transmitted through channel. In the receiver, the decoder recovers the subband signals using the received addresses and the energy information, and reconstructs the speech via the filter bank summation. As results, the coder shows fixed data rate contrary to the existing speech coders based on the non-uniform sampling. Through computer simulation, the usefulness of the proposed technique is confirmed. The signal-to-noise ratio (SNR) performance of the proposed method is comparable to that of the uniform sampled pulse code modulation (PCM) below 20 kbps data rate.

The output of pulse code modulation (PCM) encoder is statistically described; this description is used at the receiver to reduce the error of the received signal. Recently, PCM is the most important form of pulse modulation. Bit errors occur at the transmission of information through the communication system and the amount of error depends on the characteristics of the channel. In this paper, the voice message is sampled greater than Nyquist rate and quantized with scalar quantizer, then the quantized levels are encoded with resolution B bits assigned to each level. The binary data are mapped and transmitted over Gaussian noise channel. The system was designed without channel coding. At the receiver the statistical description is exploited to conceal the errors. The results show that a significant improvement gained in bit error rate that was reduced from 0.078 to 0.052 at 0 dB of Eb N0⁄ by soft decoding, compared to hard decoding.

A codec is a device which helps to encode or decode digital data signals, it encodes a data stream or encrypted form and a decoder operates a reverse process of encoder. This codec's are applied in signal editing, video editing; signal conferencing, video conferencing etc. The codec's have two major principles like pulse code modulation method and delta modulation method. In this paper, we proposed a survey of various effective codec implementation and performance analyzing methods with different applications. The various effective codec's are implemented such as CELP codec, MELP codec, SPEEX speech codec, and OPUS speech codec. A CELP codec is code excited linear prediction method which helps to increase bit detection rate, high efficient process in speech coding, and peak quality in low bit rates. A CELP codec is to be applied in real time advanced technologies such as automatic watermarking process, communication field, Zigbee applications, filtering process, power spectrum analyzing fields, quantization methods, Gaussian processing fields, ARM processors etc. A MELP is a mixed excitation liner prediction method which helps to eliminate the voicing error obtains in multiple state excitation method. A SPEEX method helps to analyze speech quality and bit rate and an OPUS is high flexible speech codec technique which plays a vital role in real time applications. These methods are implemented and performances are analyzed based on different real world applications.

Demand for wireless capsule endoscopy is increasing rapidly due to its simplicity and comfortable procedure. However, the wireless capsule endoscopy lack in complete diagnosing of gastrointestinal tract due to its limited power supply and size. Low complexity image compression algorithm plays vital role in saving power and size by reducing the data as transmitter consume 60% of capsule power. A high efficiency and lossless image compression algorithm is proposed, which is a combination of uniform quantization, simple predictive coding and Golomb Rice code. In the proposed algorithm, RGB colour image is quantized using uniform quantization. Then, differential pulse code modulation is applied, where current pixel value is subtracted with previous pixel value to provide a difference error value. The difference error value is encoded using Golomb Rice code. Several endoscopic images are considered for evaluating the performance and efficiency of proposed algorithm. The proposed algorithm provided the compression ratio of 72.5 with less computational complexity and memory usage.

Compressed sensing (CS) have gained much interest in recent years for its advantage of simultaneously acquiring and compressing signals. The acquired signals using CS need quantization to be exploitable in digital systems for storage or transmission. Split vector quantization (SVQ) is proposed to quantize the CS measurements, and a speech compression application is performed to illustrate its usefulness. The proposed method is compared with state-of-the-art quantization techniques which are scalar quantization, differential pulse code modulation, vector quantization, and multistage vector quantization for speech compression in terms of perceptual evaluation of speech quality, signal-to-noise ratio, mean square error, and execution time. Results demonstrate that SVQ represents a competitive alternative of the studied methods for CS measurements quantization.

We propose to incorporate encryption procedure into the lossy compression of voice data PCM(Pulse Code Modulation) based on the A-law approximation quantization. The proposed codec CPCM (Chaotic Pulse Code Modulaion) will join the encryption to the compression of the voice data. This scheme provides the same compression ratio given by the PCM codec, but with an unintelligible content. Comparisons with many used schemes have been made to highlight the proposed method in terms of security and rapidity. CPCM codec can be a better alternative to Compress-then-encrypt classical methods which is a time and resource consuming and non suitable for real-time multimedia secure transmission.

Presented a low cost technique for transmitting data through radio frequency antenna using low power, which is the combination of low computational complexity image compression algorithm and simple colour reproduction algorithm. In wireless capsule endoscopy, radio frequency antenna consume more than 60% of power for transmitting data and transmitting all the RGB components consume more power. In the proposed scheme, the first compressed colour frame is transmitted followed by few number of compressed grey frames. Compression algorithm is made of simplified YUV colour space, differential pulse code modulation and Golomb Rice code. At decoder dictionary based colour reproduction algorithm is used to colorize the grey scale image using colour information of original colour image. The proposed scheme lead to saving of more than 50% power of the battery, which increases the battery life from 10 h to 18 h. This extra battery life can be used for adding extra features to capsule, which help in development of diagnosing and treatment capsules.

International audience; Wireless Body Sensor Networks open up tremendous important applications such as consistent monitoring of a patient's vital signs. One of the main challenges that a Wireless Body Sensor Network faces is the transmission of the collected vital signs measurements. Data transmission is considered to be the greatest consumer of energy in a sensor node. Multiple data compression techniques have been proposed in the literature to reduce the size of the collected data. Thus, the transmission energy consumption. In this paper, we compare the performance of three resource-aware data compression techniques that are proposed in the literature and showed good results: Lightweight Temporal Compression, Differential Pulse Code Modulation and Discrete Wavelet Transform lifting scheme. Then, we propose to adapt the lossy Lightweight Temporal Compression algorithm and combine it with the lossless Differential Pulse Code Modulation algorithm in order to achieve a higher level of compression and reduce the data reconstruction error rate. To evaluate our approach, multiple series of simulation has been done on vital signs data. The results showed that our proposed compression scheme achieved a reduction by up to 95%, and reduced the transmission energy consumption by up to 5 times.

A better speech quality is necessary for transmission of speech in mobile communication systems in Digital Telephony. Nowadays, the conversion of analog speech signals into digital format is required for effective transmission of speech over different channels at far ends. The technique for conversion of speech in digital form is very old and ordinary one which is termed as pulse code modulation (PCM), but the bandwidth of the digitally converted data is too large, so a better level of compression is needed to reduce the bandwidth and enhance the capacity of channel. The compression of speech in nowadays is performed by a procedure called speech coding. In this paper, the code excited linear predictive (CELP) coding is summarized with different bit rates. The MATLAB R2016a version is used for simulating the 9.6 and 16 kbps CELP coder, and performance analysis is done with parameters MSE and SNR.

An image compressor inside wireless capsule endoscope should have low power consumption, small silicon area, high compression rate and high reconstructed image quality. Simple and efficient image compression scheme, consisting of reversible color space transformation, quantization, subsampling, differential pulse code modulation (DPCM) and Golomb---Rice encoding, is presented in this paper. To optimize these methods and combine them optimally, the unique properties of human gastrointestinal tract image are exploited. Computationally simple and suitable color spaces for efficient compression of gastrointestinal tract images are proposed. Quantization and subsampling methods are optimally combined. A hardware-efficient, locally adaptive, Golomb---Rice entropy encoder is employed. The proposed image compression scheme gives an average compression rate of 90.35 % and peak signal-to-noise ratio of 40.66 dB. ASIC has been fabricated on UMC130nm CMOS process using Faraday high-speed standard cell library. The core of the chip occupies 0.018 mm$$^2$$2 and consumes 35 $$\upmu {\text {W}}$$μW power. The experiment was performed at 2 frames per second on a $$256\times 256$$256?256 color image. The power consumption is further reduced from 35 to 9.66 $$\upmu $$μW by implementing the proposed image compression scheme using Faraday low-leakage standard cell library on UMC130nm process. As compared to the existing DPCM-based implementations, our realization achieves a significantly higher compression rate for similar area and power consumption. We achieve almost as high compression rate as can be achieved with existing DCT-based image compression methods, but with an order of reduced area and power consumption.

The problem of state dimension reduction and quantizer design under communication constraints is discussed for state estimation in quantized linear systems. Subject to the limited signal power, number and bandwidth of the parallel channels, a differential pulse code modulation (DPCM)-like structure is adopted to generate the quantized innovations as the transmitted signals, and the multi-level quantized Kalman filter (MLQ-KF) is used to serve as the pre- and post-filters. The dimension reduction matrix and quantizer are designed jointly under the MMSE criterion of estimation at the channel receiver. To demonstrate the validity of state estimation under the adopted framework, the state estimability based on quantized innovations is analyzed by using information theoretic method. This leads to a sufficient and necessary condition of a certain estimability Gramian matrix having full rank. The quantized Gramian is proved to converge to that of the original unquantized system when the quantization intervals turn to zero. Our work also provides an auxiliary analytic support for the estimation under 1-bit quantization. Simulations show that under communication constraints, the estimation performance is satisfactory when the designed dimension reduction method and quantizer are applied. The analytic conclusion of estimability is also verified by illustrative simulations.

An efficient compression algorithm for the capsule endoscopy is described in this paper. This paper consists of a simplified YUV color space, which is developed by taking endoscopy images unique properties into consideration. This is built on RGB-sYUV color conversion, differential pulse code modulation (DPCM) and Golomb-Rice encoder. This DPCM doesn't need any extra buffer memory to store one row of images and Golomb-Rice (G-R) code is simple and easily hardware implemented. This algorithm is lossless and give a compression ratio (CR) of 68.1%. It gives better results than the standard lossless algorithm regarding complexity and compression ratio in capsule endoscopy applications.

Video coding standards are primarily designed for efficient lossy compression, but it is also desirable to support efficient lossless compression within video coding standards using small modifications to the lossy coding architecture. A simple approach is to skip transform and quantization, and simply entropy code the prediction residual. However, this approach is inefficient at compression. A more efficient and popular approach is to skip transform and quantization but also process the residual block with DPCM, along the horizontal or vertical direction, prior to entropy coding. This paper explores an alternative approach based on processing the residual block with integer-to-integer (i2i) transforms. I2i transforms can map integer pixels to integer transform coefficients without increasing the dynamic range and can be used for lossless compression. We focus on lossless intra coding and develop novel i2i approximations of the odd type-3 DST (ODST-3). Experimental results with the HEVC reference software show that the developed i2i approximations of the ODST-3 improve lossless intra-frame compression efficiency with respect to HEVC version 2, which uses the popular DPCM method, by an average 2.7% without a significant effect on computational complexity., Draft consisting of 16 pages

In this paper we present a video compression algorithm based on predictive vector quantization, which is a combination of vector quantization and differential pulse code modulation. We optimized the algorithm using chroma subsampling which reduces the amount of information that needs to be processed. This allowed us to combine two color channels into one and thereby reduce the number of predictors and codebooks. Furthermore, we introduced inter-frames which only store regions that changed compared to previous frames, further decreasing the size of compressed data.

A new approach to design a DAB audio decoder is introduced to improve the quality of audio at the receiver end. Integrating an MPEG-1 Layer II (MP2) decoder and Advanced Audio Coding Low Complexity (AAC LC) decoder provides basic audio decoding for DAB in FPGA. The audio frames data are generated from DAB channel decoder are stored in RAM. The bit stream Demultiplexer parses the quantized spectrum data in the audio frame and stores them in to the audio RAM. The inverse quantization block reads the quantized spectra from the audio RAM, performs the inverse quantization computations, and writes back the result to the audio RAM. The synthesis filter reads the inverse quantized spectra from the audio RAM, generates the time domain Pulse Code Modulation (PCM) samples, and writes them back to the audio RAM. The whole projects run by using XILINX 12.1 ISE. Finally analyze the power consumption and area occupied by the designed architecture.

In this digital age, there has been a surge in the transmission of digital data. This data is not secure and it can be easily intercepted and misused by an eavesdropper. To secure this data, steganography is used. Steganography is the art and science of passing hidden messages in such a way that only sender and the intended receiver can detect the existence of the hidden audio. If the existence of a message is unknown, it makes it harder to decrypt that message. Audio steganography is the field of steganography which hides the secret message in another medium. The secret message is embedded in a host message called cover audio. This cover audio is sent to the receiver who has a key to extract the hidden audio from the cover audio. The cover audio acts as a blanket to hide the message and the key acts as an extra layer of security. Audio steganography takes the advantage of the limitations and functioning of the human auditory system. This paper introduces and explains a way to improve the security by using differential pulse code modulation (DPCM).

Large images consume more storage space needing high data rates for transmission demanding the innovation of efficient image compression systems. Owing to the massive parallel architecture and generalization ability of neural networks to memorize inputs even on untrained data, the computational simplicity of wavelets, ability of Differential Pulse Code Modulation (DPCM) to reduce the unused or redundant bits in the information, in this paper an hybrid image compression system combining the advantages of wavelets and neural networks is implemented along with Differential Pulse Code Modulation based on the predicted sample values. Scalar quantization and Huffman encoding schemes are used as well for compressing different sub bands i.e the low frequency band coefficients are compressed by the DPCM while the high frequency band coefficients are compressed using neural networks. Satisfactory reconstructed images with increased bit rates and large Peak Signal to Noise Ratio (PSNR) can be achieved with this scheme. Wavelet transform eliminates the blocking artefacts’ associated with cosine transform and neural networks minimize the Mean Square Error (MSE).Empirical analysis and metrics calculation is performed for the sake of relative analysis. General Terms Artificial Neural Networks, Fidelity, Mean Square Error, Peak Signal to Noise Ratio, Quantization and Huffman encoding.

In this paper, we explore different techniques for lossless compression of sensor data on a wireless sensor network. In this work, we applied modify Differential Pulse Code Modulation (DPCM) scheme for our baseline lossless compression. These techniques were applied on sensor types with reasonably predictable data pattern (e.g., temperature sensor, pressure sensor, etc.) allowing about 50% data compression on the original samples. In addition, we propose a new adaptive data compression algorithm based on DPCM for higher compression of sensor data. Furthermore, this work proposes a new mathematical model based on Markov model to show where the proposed algorithm is beneficial over the conventional DPCM methods.

Representing a continuous-time signal by a set of samples is a classical problem in signal processing. We study this problem under the additional constraint that the samples are quantized or compressed in a lossy manner under a limited bitrate budget. To this end, we consider a combined sampling and source coding problem in which an analog stationary Gaussian signal is reconstructed from its encoded samples. These samples are obtained by a set of bounded linear functionals of the continuous-time path, with a limitation on the average number of samples obtained per unit time available in this setting. We provide a full characterization of the minimal distortion in terms of the sampling frequency, the bitrate, and the signal's spectrum. Assuming that the signal's energy is not uniformly distributed over its spectral support, we show that for each compression bitrate there exists a critical sampling frequency smaller than the Nyquist rate, such that the distortion in signal reconstruction when sampling at this frequency is minimal. Our results can be seen as an extension of the classical sampling theorem for bandlimited random processes in the sense that it describes the minimal amount of excess distortion in the reconstruction due to lossy compression of the samples, and provides the minimal sampling frequency required in order to achieve this distortion. Finally, we compare the fundamental limits in the combined source coding and sampling problem to the performance of pulse code modulation (PCM), where each sample is quantized by a scalar quantizer using a fixed number of bits., Comment: 20 pages, 14 figures

A lossless compression scheme for the data acquired from three-axial microelectromechanical (MEMS) accelerometer is presented. Time delay estimation (TDE) was applied in conjunction with differential pulse code modulation (DPCM) as the preprocessor to entropy coding, to perform lossless compression on an embedded sensor device with limited memory and speed in real time. The essence of this method is to check if the signal exhibits certain level of periodicity, and code the differences between samples one signal period apart if it does. Limited choice of feasible mathematical operations was an important constraint implied by the architecture of the embedded sensor device. Algorithm execution time was improved by programming in assembler and avoiding unnecessary operations. Experiments have confirmed that considerable compression ratio gains can be achieved if the signal is quasiperiodic. DOI: http://dx.doi.org/10.5755/j01.eie.22.2.7646

In the last few decades, Digital image compression has received significant attention of researchers. Recently, based on wavelets there has been many compression algorithms. In comparison to other compression techniques, image compression using wavelet based algorithms lead to high compression ratios. In this paper, we have proposed a image compression algorithm which combines the feature of both wavelet transform and Radial Basis Function Neural Network along with vector quantization. First the images are decomposed into a set of subbands having different resolution with respect to different frequency bands using wavelet filters. Based on their statistical properties, different coding and quantization techniques are employed. The Differential Pulse Code Modulation (DPCM) is used to compress the low frequency band coefficients and Radial Basis Function Neural Network (RBFNN) is used to compress the high frequency band coefficients. The hidden layer coefficients of RBFNN subsequently are vector quantized so that without much degradation of the reconstructed image, the compression ratio can be increased. In terms of peak signal to noise ratio (PSNR) and computation time (CT), a large compression ratio has been achieved with satisfactory reconstructed images in relation to the existing methods by using the proposed technique.

Telemedicine means transmission of medical data between users for diagnosis is one of the emerging fields in medicine. During transmission of medical data such as images, videos etc over internet huge bandwidth are needed. Resolution factor and number of images per diagnosis makes even the size of the medical image and video that belongs to a single patient to be very large in size. So there is an immense need for efficient compression techniques for use in compressing these medical data to decrease the storage space and efficiency of transfer the images over network for access to electronic patient records. And also during transmission security of the data is very important. This project summarizes the different transformation methods used in compression as compression is one of the techniques that reduces the amount of data needed for storage or transmission of information, and the security technique used for secure transmission of data. Here we outlines the comparison of transformation methods such as DPCM (Differential Pulse Code Modulation), and prediction improvement in DPCM transformation step of compression which is efficient in both entropy reduction and computational complexity. The improved DPCM transformation increases the energy compaction of prediction model and as a result reduces entropy value of transformed image. After transforming the image Huffman encoding used to compress the image. Security is then added to the compressed image formed by signature analysis. Finally each process involved in detecting hard exudates take up a icon in a single window called Graphical User Interface (GUI) window which will be an exclusive window for detecting hard exudates. The processed resultant image can be examined comfortably at its earlier stages and thus reducing the examining time. The result is analyzed using MATLAB and implemented in hardware.

Wyner-Ziv (WZ) coding of video utilizes simple encoders and highly complex decoders. A transcoder from a WZ codec to a traditional codec can potentially increase the range of applications for WZ codecs. We present a transcoder scheme from the most popular WZ codec architecture to a differential pulse code modulation/discrete cosine transform codec. As a proof of concept, we implemented this transcoder using a simple pixel-domain WZ codec and the standard H.263+. The transcoder design aims at reducing complexity as a large amount of computation is saved by reusing the motion estimation, calculated at the side information generation process, and the l-frame streams. New approaches are used to generate side information and to map motion vectors for the transcoder. Results are presented to demonstrate the transcoder performance.

Since the structured quasi-cyclic low-density parity-check (QC-LDPC) codes for most modern wireless communication systems include multiple code rates, various block lengths, and the corresponding different sizes of submatrices in parity check matrix (PCM), the reconfigurable LDPC decoder is desirable and the permutation network is needed to accommodate any input number (IN) and shift number (SN) for cyclic shift. In this paper, we propose a novel permutation network architecture for the reconfigurable QC-LDPC decoders based on Banyan network. We prove that Banyan network has the nonblocking property for cyclic shift when the IN is power of 2, and give the control signal generating algorithm. Through introducing the bypass network, we put forward the nonblocking scheme for any IN and SN. In addition, we present the hardware design of the control signal generator, which can greatly reduce the hardware complexity and latency. The synthesis results using the TSMC 0.18 μm library demonstrate that the proposed permutation network can be implemented with the area of 0.546 mm 2 and the frequency of 292 MHz.

Images have large data quantity. For storage and transmission of images, high efficiency image compression methods are under wide attention. In this paper, we propose a neuro- wavelet based model for image compression, which combines the advantages of wavelet transform and neural network and uses fuzzy vector quantization on hidden layer coefficients. Images are decomposed using wavelet filters into a set of sub bands with different resolution corresponding to different frequency bands. Different quantization and coding schemes are used for different sub bands based on their statistical properties. The coefficients in the lowest frequency band are compressed by differential pulse code modulation (DPCM) and the coefficients in higher frequency bands are compressed using neural network. The coefficients of the hidden layer of the neural network are further fuzzy vector quantized, which increases the compression ratio. The visual quality of the image has been increased by introducing fuzziness to vector quantization algorithm. Satisfactory reconstructed images with large compression ratios have been achieved using this scheme.

Differential pulse code modulation (DPCM) is able to code highly correlated sources efficiently at high bit rates but not at low bit-rate regions. Motivated by the rate-distortion theory, a simple modified DPCM codec using multirate processing is proposed. A low-pass filter is used to limit the source signal spectrum according to the optimal mapping scheme. Bit rate reduction is achieved by reducing the number of transmitted samples through downsampling. A Wiener filter is appended at the decoder for further noise reduction. Simulation results agree with theoretical analysis and show that for a first-order autoregressive [AR(1)] process, the proposed codec makes substantial improvements over the classic DPCM and performs close to the rate-distortion bound. The proposed system is then implemented as a low-rate predictive image coder. In addition to apply two-dimensional DPCM, adaptive entropy coding is used to exploit variation in the local statistics of the prediction error image. Simulation results show that the system is able to outperform JPEG at low rates and has subjective quality comparable to that of JPEG2000.

In transmission, storaging and coding of digital signals we frequently perform A/D conversion using quantization. In this paper we study the maximal and mean square errors as a result of quantization. We focus on the sigma–delta modulation quantization scheme in the finite frame expansion setting. We show that this problem is related to the classical Traveling Salesman Problem (TSP) in the Euclidean space. It is known [Benedetto et al., Sigma–delta (\(\Sigma\Delta\)) quantization and finite frames, IEEE Trans. Inform. Theory 52, 1990–2005 (2006)] that the error bounds from the sigma–delta scheme depends on the ordering of the frame elements. By examining a priori bounds for the Euclidean TSP we show that error bounds in the sigma–delta scheme is superior to those from the pulse code modulation (PCM) scheme in general. We also give a recursive algorithm for finding an ordering of the frame elements that will lead to good maximal error and mean square error.

Pulse Code Modulation (PCM), also known as baseband transmission, converts the input analog signal into digital format and transmits over a digital communication channel. It involves: Band limiting the input analog signal Sampling Quantization A to D conversion Signal shaping

Beta-encoders with error correction were introduced by Daubechies, DeVore, Guumlntuumlrk and Vaishampayan as an alternative to pulse-code modulation (PCM) for analog-to-digital conversion. An N-bit beta-encoder quantizes a real number by computing one of its N-bit truncated beta-expansions where betaisin(1,2) determines the base of expansion. These encoders have (almost) optimal rate-distortion properties like PCM; furthermore, they exploit the redundancy of beta-expansions and thus they are robust with respect to quantizer imperfections. However, these encoders have the shortcoming that the decoder needs to know the value of the base of expansion beta, a gain factor in the circuit used by the encoder, which is an impractical constraint. We present a method to implement beta-encoders so that they are also robust with respect to uncertainties of the value of beta. The method relies upon embedding the value of beta in the encoded bitstream. We show that this can be done without a priori knowledge of beta by the transmitting party. Moreover the algorithm still works if the value of beta changes (slowly) during the implementation

In practical signal processing, it is necessary to quantize the sampled signals. Quantization is considered a necessary step to digitalize signals and realize the high-efficient transmission of digital signals. As a new signal processing theory, compressed sensing (CS) which is promoted as a joint sampling and compression approach for sparse signals has caused wide public concern in the field of image processing. In a practical application, although quantization is unavoidable for CS measurements, CS literature has largely avoided to discuss the topic of quantization. In this paper, differential pulse-code modulation(DPCM) is coupled with nonuniform scalar quantization(nonuniform SQ) to provide block-based compressed sensing (BCS) quantization of images. This paper analyzes the distribution of prediction errors in DPCM framework and draws a conclusion that in statistical sense such distribution is consistent with the characteristics of nonuniform scalar quantization. This discovery provides a theoretical basis for the proposed quantization method. Experimental results show that the proposed quantization scheme effectively increases the quantized signal to noise ratio(SNR), meanwhile improves the quality of reconstructed images.

This article presents a practical implementation of an ECG compression algorithm using a Max-Lloyd quantizer, to optimize the low resources of an ECG acquisition and transmission system (telemetry system) for dolphins and human divers. The algorithm scheme is based on a first-order differential pulse code modulation (DPCM) and uses a Max-Lloyd quantizer to code the difference between the current and predicted samples. The use of the non-uniform quantizer instead of a uniform quantizer improves the percent root mean-square difference (PRD), thereby producing a low distortion in the reconstructed signals. Due to its low computational complexity, the compression process can be accomplished on-line during the ECG acquisition process.

A clustered differential pulse code modulation lossless compression method for hyperspectral images is presented. The spectra of a hyperspectral image is clustered, and an optimized predictor is calculated for each cluster. Prediction is performed using a linear predictor. After prediction, the difference between the predicted and original values is computed. The difference is entropy-coded using an adaptive entropy coder for each cluster. The achieved compression ratios presented here are compared with those of existing methods. The results show that the proposed lossless compression method for hyperspectral images outperforms previous methods.

In the Digitalized world, the problem exists in transmitting and concealing highly secured and important information. This results in developing better technique/algorithm for the secure and safe transmission of information with data hiding. Here comes our study in Audio Steganography. With the existing scenario we say that Differential Pulse Code Modulation (DPCM) is better than Pulse Code Modulation (PCM). If Steganography is implemented in the audio signal, the above result is contrary. We focus on the audio steganography for analyzing the behavior for different modulation schemes. The result is controversial (i.e.) PCM is better option for transmitting an encoded audio signal with text information. This result has not been proved yet in terms of coding. The coding is developed in MATLAB. In the algorithm, using the scattering method, the encoded text is embedded into digitized audio signal. Here comes the case study of each method of modulation, ultimately proving that PCM is better method compared to Differential PCM (DPCM).

Near-lossless compression yielding strictly bounded reconstruction error is proposed for high-quality compression of remote sensing images. A classified causal differential pulse code modulation scheme is presented for optical data, either multi/hyperspectral three-dimensional (3-D) or panchromatic two-dimensional (2-D) observations. It is based on a classified linear-regression prediction, followed by context-based arithmetic coding of the outcome prediction errors and provides excellent performances, both for reversible and for irreversible (near-lossless) compression. Coding times are affordable thanks to fast convergence of training. Decoding is always real time. If the reconstruction errors fall within the boundaries of the noise distributions, the decoded images will be virtually lossless even though encoding was not strictly reversible.

Remote sensing satellites use onboard compression techniques to overcome the limited bandwidth and increasing data volume requirements of images. On-board compression using Differential Pulse Code Modulation (DPCM) is implemented for Resourcesat-2, LISS-3 and LISS-4 sensors. Implemented DPCM is a lossy compression and hence renders artifacts, when images are decompressed on ground. In this paper a technique for restoration of DPCM artifacts is proposed and its performance is evaluated using Mean Square Error (MSE) and Peak Signal to Noise Ratio (PSNR). Proposed technique is used as part of operational data products generation software and sample results are shown.

This paper presents a new motion-compensated subband video-coding technique using variable reference-frame positions for motion estimation. The work builds on temporal segmentation for determining the reference-frame positions and incorporates multiresolution motion estimation in the subband domain. The key advantage of the new algorithm is the complexity reduction in motion estimation that uses the multiresolution property of subband decomposition. Temporal segmentation is performed with the lowest spatial subband, based on the detection of two types of scene change. The frames of input video are split into seven spatial subbands, and the motion vectors for each subband are generated by a hierarchical motion-estimation algorithm. Blockwise differential pulse code modulation (DPCM) and a uniform quantizer are used for the lowest subband of the first frame only, and the subbands of all other cases are coded by PCM with a dead zone quantizer. Simulation results show that our scene-adaptive scheme compares favorably with a fixed interpolation structure.

A near-lossless image compression scheme is presented. It is essentially a differential pulse code modulation (DPCM) system with a mechanism incorporated to minimize the entropy of the quantized prediction error sequence. With a "near-lossless" criterion of no more than a d gray-level error for each pixel, where d is a small nonnegative integer, trellises describing all allowable quantized prediction error sequences are constructed. A set of "contexts" is defined for the conditioning prediction error model and an algorithm that produces minimum entropy conditioned on the contexts is presented. Finally, experimental results are given.

Dolby AC-3 and MPEG-2 audio are two different standards for the compression of up to six independent digital audio channels into a single serial bit stream. This paper describes a DSP-based integrated circuit which implements the two decoding algorithms, allowing original pulse code modulation (PCM) information to be recovered from an encoded bit stream. We mainly describe the methodology which was employed to design and validate the software embedded on the chip.

In personal communication systems, better voice quality and lower terminal price are essential requirements, and adaptive differential pulse-code modulation (ADPCM) codecs are one of the voice codec candidates that have good voice quality and reasonable cost. However, in slow fading environments, burst errors induce click noise in ADPCM voice signal transmission. This paper proposes a novel error-concealment scheme to improve the ADPCM voice signal transmission quality of time division multiple access time division duplex (TDMA-TDD) personal communication systems in slow fading environments. The proposed scheme employs an improved error-detection technique with PCM differential detection and overflow detection coupled with a cyclic redundancy check (CRC) code. To compensate for residual click noise, an expanded CRC error-detection signal is used. Erroneous PCM signals are adaptively suppressed according to the click-noise detection information. Hardware simulation results show that these techniques significantly improve ADPCM voice signal transmission quality by 0.5 points and 1.0 compared to that of the conventional ADPCM muting scheme and no processing, respectively, with the mean opinion scoring (MOS) test in the slow fading environments typical of personal communication systems.

Real-time audio transmission requires good quality for a restricted channel capacity and minimum latency. An ultra-low delay audio coding scheme based on differential pulse code modulation (DPCM) and block companded quantization is presented. The prediction filter of the base backward DPCM codec is attained as a FIR filter in lattice structure. The proposed block based audio coding introduces an algorithmic latency below one millisecond and an overhead of less than a half bit per sample. The advantage of the block companded quantization with DPCM is the capability to follow rapid changes in the signal. Therefore, it significantly improves the perceptual audio quality compared to a plain DPCM coding scheme with an adaptive quantizer.

This paper presents a simple technique for the design of a phase-modulated residual carrier communications link to mitigate mutual interferences among the channels for optimum system performance. The emphasis is on two data channels which am operated simultaneously with a ranging signal. The data channels employ PCM/PM and PCM/PSK/PM modulation schemes for high and low data rate channels, respectively. The technique proposed selects the optimum (1) subcarrier frequency to reduce the interference between the two data channels, and (2) modulation indices to suppress the undesired signals in the data channels. Further, this technique also provides an optimum balance of power between the two telemetry data channels and the ranging channel. Although this technique is proposed to optimize the performance degradation for two data channels, generalizations can be made for more than two data channels. To make the generalizations easier, this paper presents an algorithm based on the proposed technique, along with numerical results demonstrating its applicability. >

The error correcting capability of the unequal error protecting (UEP) Codes is described in terms of correcting errors in specific digits of a codeword even though other digits may be decoded incorrectly. We investigate the overall distortion when these codes are applied to transmission of analog sources in a communication system. Simulation and analytic results indicate the performance improvement gained by using UEP codes for various sources and source encoding structures. >