As a wide used protocol in industry, BMS and T&D system, I2C bus with isolation features stands out to provide additional safety protection and noise immunity. This paper introduces an implementation ...method of the isolated I2C interface device with high reliability and low delay. This device can meet the I2C ultra-high speed working mode with communication frequency of 1.7MHz as well as prevent the occurrence of interlock in bi-directional communication. The device is fabricated in 0.18um CMOS process.
We present a generic and real-time time-varying point cloud codec for 3D immersive video. This codec is suitable for mixed reality applications in which 3D point clouds are acquired at a fast rate. ...In this codec, intra frames are coded progressively in an octree subdivision. To further exploit interframe dependencies, we present an inter-prediction algorithm that partitions the octree voxel space in N × N × N macroblocks (N = 8, 16, 32). The algorithm codes points in these blocks in the predictive frame as a rigid transform applied to the points in the intra-coded frame. The rigid transform is computed using the iterative closest point algorithm and compactly represented in a quaternion quantization scheme. To encode the color attributes, we defined a mapping of color per vertex attributes in the traversed octree to an image grid and use legacy image coding method based on JPEG. As a result, a generic compression framework suitable for realtime 3D tele-immersion is developed. This framework has been optimized to run in real time on commodity hardware for both the encoder and decoder. Objective evaluation shows that a higher rate-distortion performance is achieved compared with available point cloud codecs. A subjective study in a state-of-the-art mixed reality system shows that introduced prediction distortions are negligible compared with the original reconstructed point clouds. In addition, it shows the benefit of reconstructed point cloud video as a representation in the 3D virtual world. The codec is available as open source for integration in immersive and augmented communication applications and serves as a base reference software platform in JTC1/SC29/WG11 (MPEG) for the further development of standardized point-cloud compression solutions.
Deep learning, e.g., convolutional neural networks (CNNs), has achieved great success in image processing and computer vision especially in high-level vision applications, such as recognition and ...understanding. However, it is rarely used to solve low-level vision problems such as image compression studied in this paper. Here, we move forward a step and propose a novel compression framework based on CNNs. To achieve high-quality image compression at low bit rates, two CNNs are seamlessly integrated into an end-to-end compression framework. The first CNN, named compact convolutional neural network (ComCNN), learns an optimal compact representation from an input image, which preserves the structural information and is then encoded using an image codec (e.g., JPEG, JPEG2000, or BPG). The second CNN, named reconstruction convolutional neural network (RecCNN), is used to reconstruct the decoded image with high quality in the decoding end. To make two CNNs effectively collaborate, we develop a unified end-to-end learning algorithm to simultaneously learn ComCNN and RecCNN, which facilitates the accurate reconstruction of the decoded image using RecCNN. Such a design also makes the proposed compression framework compatible with existing image coding standards. Experimental results validate that the proposed compression framework greatly outperforms several compression frameworks that use existing image coding standards with the state-of-the-art deblocking or denoising post-processing methods.
Multiple description coding (MDC) is able to stably transmit signal in un-reliable and non-prioritized networks, which has been broadly studied for several decades. However, traditional MDC does not ...well leverage image's context features to generate multiple descriptions. In this paper, we propose a novel standard-compliant convolutional neural network-based MDC framework, which efficiently leverages image's context information to compress the image. First, multiple description generator network (MDGN) is designed to produce appearance-similar yet feature-different multiple descriptions automatically according to image's content, which are compressed by a standard codec. Second, we present multiple description reconstruction network (MDRN) including side reconstruction networks (SRNs) and central reconstruction network (CRN). When any one of two lossy descriptions is received at decoder, SRN network is used to improve the quality of this decoded lossy description by simultaneously removing compression artifact and up-sampling. Meanwhile, we utilize CRN network with two decoded descriptions as inputs for better reconstruction, if both of lossy descriptions are available. Third, multiple description virtual codec network is proposed to bridge the gap between MDGN network and MDRN network in order to train an end-to-end MDC framework. Here, two learning algorithms are provided to train our whole framework. In addition to structural dis-similarity loss function, the produced descriptions are used as opposing labels with multiple description distance loss function to regularize the training of MDGN network. These losses guarantee that the generated descriptions are structurally similar yet finely diverse. Experimental results show a great deal of objective and subjective quality measurements to validate the effectiveness of our framework.
A New Parallel CODEC Technique for CDMA NoCs Wang, Jian; Guo, Shize; Chen, Zhe ...
IEEE transactions on industrial electronics (1982),
08/2018, Letnik:
65, Številka:
8
Journal Article
Recenzirano
Code division multiple access (CDMA) network-on-chip (NoC) has been proposed for many-core systems due to its data transfer parallelism over communication channels. Consequently, coder-decoder ...(CODEC) module, which greatly impacts the performance of CDMA NoCs, attracted growing attention in recent years. In this paper, we propose a new parallel CODEC technique for CDMA NoCs. In general, by using a few simple logic circuits with small penalties in area and power, our new parallel (NPC) CODEC can execute the encoding/decoding process in parallel and thus reduce the data transfer latency. To reveal the benefits of our method for on-chip communication, we apply our NPC to CDMA NoCs and perform extensive experiments. From the results, we can find that our method outperforms existing parallel CODECs, such as Walsh-based parallel CODEC (WPC) and overloaded parallel CODEC (OPC). Specifically, it improves the critical point of communication latency (7.3% over WPC and 13.5% over OPC), reduces packet latency jitter by about 17.3% (against WPC) and 71.6% (against OPC), and improves energy efficiency by up to 41.2% (against WPC) and 59.2% (against OPC).
Decimation of a discrete-time signal below the Nyquist rate without applying an appropriate lowpass filter results in a distortion called aliasing. If wideband speech sampled at 16 kHz is decimated ...by 2 to result in a signal sampled at 8 kHz with aliasing, the decimated signal would be the summation of two speech-like signals, which are the narrowband speech covering 0-4 kHz and the spectrally flipped aliasing component coming from 8-4 kHz. Recently, the performance of speech separation has been remarkably improved with deep learning-based approaches, implying that the narrowband and aliasing components may be able to be separated. In this letter, we propose a novel method for low-rate wideband speech coding utilizing a standard narrowband codec. Instead of coding wideband speech using a wideband codec with a limited bitrate, we propose to decimate the input wideband speech incurring aliasing, and then encode it with a narrowband codec by allocating all the allowed bitrate to 0-4 kHz. After decoding the encoded bitstream, we apply a speech separation technique to obtain the narrowband and aliasing signals, which are then used to reconstruct the wideband speech by expansion, low/highpass filtering, and summation. Experimental results showed that the proposed method could achieve subjective quality comparable to the speeches coded by wideband codecs at higher bitrates in a subjective MUSHRA test.
This paper provides an overview of Scalable High efficiency Video Coding (SHVC), the scalable extensions of the High Efficiency Video Coding (HEVC) standard, published in the second version of HEVC. ...In addition to the temporal scalability already provided by the first version of HEVC, SHVC further provides spatial, signal-to-noise ratio, bit depth, and color gamut scalability functionalities, as well as combinations of any of these. The SHVC architecture design enables SHVC implementations to be built using multiple repurposed single-layer HEVC codec cores, with the addition of interlayer reference picture processing modules. The general multilayer high-level syntax design common to all multilayer HEVC extensions, including SHVC, MV-HEVC, and 3D HEVC, is described. The interlayer reference picture processing modules, including texture and motion resampling and color mapping, are also described. Performance comparisons are provided for SHVC versus simulcast HEVC and versus the scalable video coding extension to H.264/advanced video coding.
Video content is routinely acquired and distributed in a digital compressed format. In many cases, the same video content is encoded multiple times. This is the typical scenario that arises when a ...video, originally encoded directly by the acquisition device, is then re-encoded, either after an editing operation, or when uploaded to a sharing website. The analysis of the bitstream reveals details of the last compression step (i.e., the codec adopted and the corresponding encoding parameters), while masking the previous compression history. Therefore, in this paper, we consider a processing chain of two coding steps, and we propose a method that exploits coding-based footprints to identify both the codec and the size of the group of pictures (GOPs) used in the first coding step. This sort of analysis is useful in video forensics, when the analyst is interested in determining the characteristics of the originating source device, and in video quality assessment, since quality is determined by the whole compression history. The proposed method relies on the fact that lossy coding is an (almost) idempotent operation. That is, re-encoding a video sequence with the same codec and coding parameters produces a sequence that is similar to the former. As a consequence, if the second codec in the chain does not significantly alter the sequence, it is possible to analyze this sort of similarity to identify the first codec and the adopted GOP size. The method was extensively validated on a very large data set of video sequences generated by encoding content with a diversity of codecs (MPEG-2, MPEG-4, H.264/AVC, and DIRAC) and different encoding parameters. In addition, a proof of concept showing that the proposed method can also be used on videos downloaded from YouTube is reported.
Despite the recent progress on neural network architectures for speech separation, the balance between the model size, model complexity and model performance is still an important and challenging ...problem for the deployment of such models to low-resource platforms. In this paper, we propose two simple modules, group communication and context codec, that can be easily applied to a wide range of architectures to jointly decrease the model size and complexity without sacrificing the performance. A group communication module splits a high-dimensional feature into groups of low-dimensional features and captures the inter-group dependency. A separation module with a significantly smaller model size can then be shared by all the groups. A context codec module, containing a context encoder and a context decoder, is designed as a learnable downsampling and upsampling module to decrease the length of a sequential feature processed by the separation module. The combination of the group communication and the context codec modules is referred to as the GC3 design. Experimental results show that applying GC3 on multiple network architectures for speech separation can achieve on-par or better performance with as small as 2.5% model size and 17.6% model complexity, respectively.
Compressing an image with more bits automatically allocated to the region of interest (ROI) than to the background can both protect key information and reduce substantial redundancy. This paper ...models ROI image compression as an optimization problem of minimizing a weighted sum of the rate of the image and distortion of the ROI. The traditional framework solves this problem by cascading ROI prediction and ROI coding, through which achieving the optimized solution is impossible. To improve coding performance, we propose a novel deep-learning-based unified framework that can achieve rate distortion optimization for ROI compression. Specifically, the proposed framework includes a pair of ROI encoder and decoder convolutional neural networks and a learned entropy codec. The encoder network simultaneously generates multiscale representations that support efficient rate allocation and an implicit ROI mask that guides rate allocation. The proposed framework can automatically complete ROI image compression, and it can be optimized from data in an end-to-end manner. To effectively train the framework by back propagation, we develop a soft-to-hard ROI prediction scheme to make the entire framework differential. To improve visual quality, we propose a hierarchical distortion loss function to protect both pixel-level fidelity for ROI and structural similarity for the entire image. The proposed framework is implemented in two scenarios: salient-target and face-target ROI compression. Comparative experiments demonstrate the advantages of the proposed framework over the traditional framework, including considerably better subjective visual quality, significantly higher objective ROI compression performance and execution efficiency.
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