CAS-CNN: A Deep Convolutional Neural Network for Image Compression Artifact Suppression

Lossy image compression algorithms are pervasively used to reduce the size of images transmitted over the web and recorded on data storage media. However, we pay for their high compression rate with visual artifacts degrading the user experience. Deep convolutional neural networks have become a widespread tool to address high-level computer vision tasks very successfully. Recently, they have found their way into the areas of low-level computer vision and image processing to solve regression problems mostly with relatively shallow networks. We present a novel 12-layer deep convolutional network for image compression artifact suppression with hierarchical skip connections and a multi-scale loss function. We achieve a boost of up to 1.79 dB in PSNR over ordinary JPEG and an improvement of up to 0.36 dB over the best previous ConvNet result. We show that a network trained for a specific quality factor (QF) is resilient to the QF used to compress the input image - a single network trained for QF 60 provides a PSNR gain of more than 1.5 dB over the wide QF range from 40 to 76.Comment: 8 page

Image compression based on 2D Discrete Fourier Transform and matrix minimization algorithm

In the present era of the internet and multimedia, image compression techniques are essential to improve image and video performance in terms of storage space, network bandwidth usage, and secure transmission. A number of image compression methods are available with largely differing compression ratios and coding complexity. In this paper we propose a new method for compressing high-resolution images based on the Discrete Fourier Transform (DFT) and Matrix Minimization (MM) algorithm. The method consists of transforming an image by DFT yielding the real and imaginary components. A quantization process is applied to both components independently aiming at increasing the number of high frequency coefficients. The real component matrix is separated into Low Frequency Coefficients (LFC) and High Frequency Coefficients (HFC). Finally, the MM algorithm followed by arithmetic coding is applied to the LFC and HFC matrices. The decompression algorithm decodes the data in reverse order. A sequential search algorithm is used to decode the data from the MM matrix. Thereafter, all decoded LFC and HFC values are combined into one matrix followed by the inverse DFT. Results demonstrate that the proposed method yields high compression ratios over 98% for structured light images with good image reconstruction. Moreover, it is shown that the proposed method compares favorably with the JPEG technique based on compression ratios and image quality

Development of control algorithm for a new 12s-6p single phase field excited flux switching motor

Flux switching motor (FSM) fall into a special category of switch reluctance motors (SRM). One of the key features of FSM is its rotor structure. Generally, it is free from any magnet and winding. Thus, allowing the motor to attain considerably higher speed and more stability then conventional AC motor. However, this simple and robust structure demands more sophisticated driving mechanism mainly due to the absence of rotating magneto motive force (MMF) in the rotor. The main concern of this research is to design algorithms for starting and driving 12 slots and 6 poles (12S-6P) segmental rotor field excited flux switching motor (FEFSM) and evaluate the algorithms efficiency by analyzing motor’s dynamic performance in terms of torque and current consumption. In this research, two algorithms have been proposed in which first algorithm is based on bipolar DC signals while second algorithm is based on field oriented control (FOC) principle. For position detection, algorithms merely need a basic infrared transceiver sensor. Bipolar DC signal algorithm is based on changing the polarity of armature DC voltage on the detection of zero rotor position. On the other hand, FOC algorithm involves detection of rotor zero position to estimate speed and prediction of instantaneous rotor position in real time. Initially, fundamental control principle for 12S-6P FEFSM has been identified through the finite element analysis (FEA) of the model. Afterwards control algorithms have been successfully developed and implemented in the motor control hardware. Compared to Bi-polar DC algorithm, the observations shows that the single phase FOC algorithm results in far less distortion of armature voltage waveforms even at high speed, which results in jittering free motor operation. On the other hand, Bi-polar DC algorithm results in much higher torque production, which is about 50% more than that of the single phase FOC’s yield. In terms of simulation and prototype performance comparison, Bi-polar DC algorithm is about 92% efficient in torque generation in case of initial model of FEFSM and staggering efficiency around 96% in case of optimized motor model