Low-complexity Encoding Research Articles

Cloud-Based Distributed Image Coding

With multimedia flourishing on the Web, it is easy to find similar images for a query, especially landmark images. Traditional image coding, such as JPEG, cannot exploit correlations with external images. Existing vision-based approaches are able to exploit such correlations by reconstructing from local descriptors but cannot ensure the pixel-level fidelity of the reconstruction. In this paper, a cloud-based distributed image coding (Cloud-DIC) scheme is proposed to exploit external correlations for mobile photo uploading. For each input image, a thumbnail is transmitted to retrieve correlated images and reconstruct it in the cloud by geometrical and illumination registrations. Such a reconstruction serves as the side information (SI) in the Cloud-DIC. The image is then compressed by a transform-domain syndrome coding to correct the disparity between the original image and the SI. Once a bitplane is received in the cloud, an iterative refinement process is performed between the final reconstruction and the SI. Moreover, a joint encoder/decoder mode decision at block, frequency, and bitplane levels is proposed to adapt to different correlations. Experimental results on a landmark image database show that the Cloud-DIC can largely enhance the coding efficiency both subjectively and objectively, with up to 5-dB gains and 70% bits saving over JPEG with arithmetic coding, and perform comparably at low bitrates with the intra coding of the High Efficiency Video Coding standard with a much lower encoder complexity.

Construction of Polar Codes Exploiting Channel Transformation Structure

Polar Codes are the first binary linear codes that provably achieve the symmetric capacity of arbitrary binary-input discrete memoryless channels. Their construction is explicit as implied by the transformations that lead to channel polarization, and low-complexity encoding and decoding methods are available. While being explicitly defined, exact construction is intractable as it depends on calculating channel parameters of channels whose output alphabets grow exponentially in the code length. To overcome this problem, heuristics have been proposed. In this work, we show how to accelerate one popular construction method by introducing two modifications to the channel transformation step, leaving quality of the results unaffected.

Distributed Compressive Sensing of Hyperspectral Images Using Low Rank and Structure Similarity Property

An efficient method and system for distributed compressive sensing of hyperspectral images is presented, which exploit the low rank and structure similarity property of hyperspectral imagery. In this paper, by integrating the respective characteristics of DSC and CS, a distributed compressive sensing framework is proposed to simultaneously capture and compress hyperspectral images. At the encoder, every band image is measured independently, where almost all computation burdens can be shifted to the decoder, resulting in a very low-complexity encoder. It is simple to operate and easy to hardware implementation. At the decoder, each band image is reconstructed by the method of total variation norm minimize. During each band reconstruction, the low rand structure of band images and spectrum structure similarity are used to give birth to the new regularizers. With combining the new regularizers and other regularizer, we can sufficiently exploit the spatial correlation, spectral correlation and spectral structural redundancy in hyperspectral imagery. A numerical optimization algorithm is also proposed to solve the reconstruction model by augmented Lagrangian multiplier method. Experimental results show that this method can effectively improve the reconstruction quality of hyperspectral images.

Model-based low bit-rate video coding for resource-deficient wireless visual communication

In this paper, an effective low bit-rate video coding scheme is developed to realize state-of-the-art video coding efficiency with lower encoder complexity, while supporting standard compliance and error resilience. Such an architecture is particularly attractive for application scenarios involving resource-deficient wireless video communications. At the encoder, in order to increase resilience to channel error, multiple descriptions of a video sequence are generated in the spatio-temporal domain by temporal multiplexing and spatial adaptive downsampling. The resulting side descriptions are interleaved with each other in temporal domain, while still with conventional square sample grids in spatial domain. As such, each side description can be compressed without any change to existing video coding standards. At the decoder, each side description is first decompressed, and then reconstructed to the original resolution with the help of the other side description. In this procedure, the decoder recovers the original video sequence in a constrained least squares regression process, in which 2D or 3D piecewise autoregressive model is adaptively chosen according to different predictive modes. In this way, the spatial and temporal correlation is sufficiently explored to achieve superior quality. Experimental results demonstrate that the proposed video coding scheme outperforms H.264/AVC and other state-of-the-art methods in rate–distortion performance at low bit-rates and achieves superior visual quality at medium bit rates as well, while with lower encoding computational complexity.

Resumable Load Data Compression in Smart Grids

We propose a compression approach for load profile data, which addresses practical requirements of smart metering. By providing linear time complexity with respect to the input data size, our compression approach is suitable for low-complexity encoding and decoding for storage and transmission of load profile data in smart grids. Furthermore, it allows for resumability with very low overhead on error-prone transmission lines, which is an important feature not available for standard time series compression schemes. In terms of compression efficiency, our approach outperforms transmission encodings that are currently used for electricity metering by an order of magnitude.

Global Motion Assisted Low Complexity Video Encoding for UAV Applications

We design a video encoding scheme that is suited for applications such as unmanned aerial vehicle (UAV) video surveillance where the encoder complexity needs to be low. Our low complexity encoder predicts frames using the global motion information available in UAVs and thus achieves lower complexity and more than 40% BD-rate savings for fly-over videos compared to a complexity-constrained H.264 encoder with motion estimation restricted to 8 ×8 blocks and half pixel accuracy. We also incorporate a spectral entropy based bit allocation scheme into this encoder to achieve near constant quality within groups of pictures (GOPs) at the cost of small increases in delay and complexity, and a small drop in compression efficiency. Both these encoders with their corresponding low complexity “matched” decoders provide significant gains of more than 49% BD-rate savings over the Wyner-Ziv based DISCOVER codec which has a low complexity encoder and a high complexity decoder. Furthermore, for videos where the global motion is spatially consistent within 2 ×2 blocks, we show that the computational complexity of these proposed encoders can be significantly reduced with only about 1% BD-rate increase.

Low-Complexity Encoding of Quasi-Cyclic Codes Based on Galois Fourier Transform

This paper presents a novel low-complexity encoding algorithm for binary quasi-cyclic (QC) codes based on matrix transformation. First, a message vector is encoded into a transformed codeword in the transform domain. Then, the transmitted codeword is obtained from the transformed codeword by the inverse Galois Fourier transform. Moreover, a simple and fast mapping is devised to post-process the transformed codeword such that the transmitted codeword is binary as well. The complexity of our proposed encoding algorithm is less than ek(n-k)log2 e+ne(log22 e+log2 e)+ n/2 elog32 e bit operations for binary codes. This complexity is much lower than its traditional complexity 2e2(n - k)k. In the examples of encoding the binary (4095, 2016) and (15500, 10850) QC codes, the complexities are 12.09% and 9.49% of those of traditional encoding, respectively.

“Soft Error” Correction Method Based on Low Complexity Coding and Encoding Algorithm

SEU is one of the major challenges affecting the reliability of computers on-board. In this paper, we design a kind of encoding and decoding algorithms with a low complexity based on the data correction method to resolve the data stream errors SEU may bring. Firstly, we use the theory of linear block codes to analyze various methods of data fault tolerance, and then from the encoding and decoding principle of linear block codes we design a kind of encoding and decoding algorithms with a low complexity of linear block code, The fault-tolerant coding method can effectively correct single-bit data errors caused by SEU, with low fault-tolerant overhead. Fault injection experiments show that: this method can effectively correct data errors caused by single event upset. Compared with other common error detection or correction methods, error correction performance of this method is superior, while its fault tolerance cost is less.

Re-estimation of Motion and Reconstruction for Distributed Video Coding

Transform domain Wyner-Ziv (TDWZ) video coding is an efficient approach to distributed video coding (DVC), which provides low complexity encoding by exploiting the source statistics at the decoder side. The DVC coding efficiency depends mainly on side information and noise modeling. This paper proposes a motion re-estimation technique based on optical flow to improve side information and noise residual frames by taking partially decoded information into account. To improve noise modeling, a noise residual motion re-estimation technique is proposed. Residual motion compensation with motion updating is used to estimate a current residue based on previously decoded frames and correlation between estimated side information frames. In addition, a generalized reconstruction algorithm to optimize a multihypothesis reconstruction is proposed. The proposed techniques using motion and reconstruction re-estimation (MORE) are integrated in the SING TDWZ codec, which uses side information and noise learning. For Wyner-Ziv frames using GOP size 2, the MORE codec significantly improves the TDWZ coding efficiency with an average (Bjøntegaard) PSNR improvement of 2.5 dB and up to 6 dB improvement compared with DISCOVER.

Low-Complexity Encoding in Block-Based Hybrid Video Codecs by Moving Motion Estimation to Decoder Side

In this paper the authors present an approach to provide efficient low-complexity encoding for the block-based video coding scheme. The authors present a method based on removing the most time-consuming task, that is motion estimation, from the encoder. Instead the decoder will perform motion prediction based on the available decoded frame and send the predicted motion vectors to the encoder. The results presented are based on a modified H.264 implementation. The results show that this approach can provide rather good coding efficiency even for relatively high network delays.

Correlation Noise-Based Unequal Error Protected Rate-Adaptive Codes for Distributed Video Coding

Distributed video coding (DVC) is a paradigm that can shift most of the computational intensive tasks from the encoder to the decoder. This allows for the design of low complexity encoders that can be deployed in devices equipped with limited resources. However, the compression efficiency obtained using practical DVC codecs are still distant from those of traditional predictive video coding schemes such as H.264/AVC. One of the limitations of the existing DVC architectures is that they consider the correlation noise to be randomly distributed across the whole video frame. This paper shows that the Syner–Ziv (WZ) values that are closer to the endpoints of the quantization intervals have a higher probability of producing incorrect side information (SI) predictions. Thus, through this knowledge, rate-adaptive low-density parity-check accumulate codes that provide a higher level of protection to the unreliable SI bits can be exploited. Experimental results show that the proposed scheme can reduce the WZ bit-rate up to 13% relative to the state-of-the-art DISCOVER architecture when considering interpolation techniques and by up to 0.9 dB for extrapolation techniques.

On the Performance of Diagonal Lattice Space-Time Codes

There has been tremendous work done on designing space-time codes for the quasi-static multiple-input multiple output (MIMO) channel. All the coding design up-to-date focuses on either high-performance, high rates, low complexity encoding and decoding, or targeting a combination of these criteria . In this paper, we analyze in details the performance limits of diagonal lattice space-time codes under lattice decoding. We present both lower and upper bounds on the average decoding error probability. We first derive a new closed-form expression for the lower bound using the so-called sphere lower bound. This bound presents the ultimate performance limit a diagonal lattice space-time code can achieve at any signal-to-noise ratio (SNR). The upper bound is then derived using the union-bound which demonstrates how the average error probability can be minimized by maximizing the minimum product distance of the code. Combining both the lower and the upper bounds on the average error probability yields a simple upper bound on the the minimum product distance that any (complex) lattice code can achieve. At high-SNR regime, we discuss the outage performance of such codes and provide the achievable diversity-multiplexing tradeoff under lattice decoding.

Encoder-driven rate control and mode decision for distributed video coding

To provide low-complexity encoding for video in unidirectional or offline compression scenarios, this paper proposes an efficient feedback-channel-free distributed video coding architecture featuring a novel encoder-driven rate control scheme in tandem with a designated mode selection process. To this end, the encoder features a novel low-complexity motion estimation technique to approximate the side-information (SI) available at the decoder. Then, a SI-dependent correlation channel estimation between the approximated SI and the original frames is used to derive the theoretically required rate for successful Slepian-Wolf (SW) decoding. Based on the evaluation of the expected trade-off between the estimated required coding rate and the estimated distortion outcome, a novel encoder-side mode decision module assigns a different coding mode to distinct portions of the coded frames. In this context, skip, intra and SW coding modes are supported. To reduce the effect of underestimation, the final SW rate is adjusted upwards using a novel rate formula. Additionally, a successive SI refinement technique is exploited at the decoder to decrease the number of SW decoding failures. Experimental results illustrate the benefit of the different coding options and show similar or superior compression performance with respect to the feedback-based DISCOVER benchmark system. Finally, the low-complexity encoding characteristics of the proposed system are confirmed, as well as the beneficial impact of the proposed scheme on the decoding complexity.

Control-Point Representation and Differential Coding Affine-Motion Compensation

The affine-motion model is able to capture rotation, zooming, and the deformation of moving objects, thereby providing a better motion-compensated prediction. However, it is not widely used due to difficulty in both estimation and efficient coding of its motion parameters. To alleviate this problem, a new control-point representation that favors differential coding is proposed for efficient compression of affine parameters. By exploiting the spatial correlation between adjacent coding blocks, motion vectors at control points can be predicted and thus efficiently coded, leading to overall improved performance. To evaluate the proposed method, four new affine prediction modes are designed and embedded into the high-efficiency video coding test model HM1.0. The encoder adaptively chooses whether to use the new affine mode in an operational rate-distortion optimization. Bitrate savings up to 33.82% in low-delay and 23.90% in random-access test conditions are obtained for low-complexity encoder settings. For high-efficiency settings, bitrate savings up to 14.26% and 4.89% for these two modes are observed.

Low‐complexity distributed multiple description coding for wireless video sensor networks

Quality-of-service-guaranteed video communication in wireless video sensor networks (WVSNs) is extremely challenging because of the unique constraints of WVSN (e.g. limited resources of sensors and high error rates in wireless sensor networks) and the characteristics of video traffic (e.g. huge data rate and tight delay bounds). Distributed video coding (DVC) has emerged as a new video coding paradigm for video applications with resource-limited encoders. However, current DVC architectures still have several technical limitations that prevent their widespread use in real-world applications. A low-complexity distributed multiple description coding method that combines the principles of multiple description coding and DVC has been proposed to further improve the error resilience of DVC, while maintaining low encoder complexity and good rate-distortion performance in WVSN. Simulation results show that the proposed method is robust against transmission errors, while maintaining low encoder complexity and low system latency for resource-limited applications in WVSNs.

Low-Density Parity-Check Codes from Transversal Designs with Improved Stopping Set Distributions

This paper examines the construction of low-density parity-check (LDPC) codes from transversal designs based on sets of mutually orthogonal Latin squares (MOLS). By transferring the concept of configurations in combinatorial designs to the level of Latin squares, we thoroughly investigate the occurrence and avoidance of stopping sets for the arising codes. Stopping sets are known to determine the decoding performance over the binary erasure channel and should be avoided for small sizes. Based on large sets of simple-structured MOLS, we derive powerful constraints for the choice of suitable subsets, leading to improved stopping set distributions for the corresponding codes. We focus on LDPC codes with column weight 4, but the results are also applicable for the construction of codes with higher column weights. Finally, we show that a subclass of the presented codes has quasi-cyclic structure which allows low-complexity encoding.

An image compression method for space multispectral time delay and integration charge coupled device camera

Multispectral time delay and integration charge coupled device (TDICCD) image compression requires a low-complexity encoder because it is usually completed on board where the energy and memory are limited. The Consultative Committee for Space Data Systems (CCSDS) has proposed an image data compression (CCSDS-IDC) algorithm which is so far most widely implemented in hardware. However, it cannot reduce spectral redundancy in multispectral images. In this paper, we propose a low-complexity improved CCSDS-IDC (ICCSDS-IDC)-based distributed source coding (DSC) scheme for multispectral TDICCD image consisting of a few bands. Our scheme is based on an ICCSDS-IDC approach that uses a bit plane extractor to parse the differences in the original image and its wavelet transformed coefficient. The output of bit plane extractor will be encoded by a first order entropy coder. Low-density parity-check-based Slepian—Wolf (SW) coder is adopted to implement the DSC strategy. Experimental results on space multispectral TDICCD images show that the proposed scheme significantly outperforms the CCSDS-IDC-based coder in each band.

Efficient Multiternary Digit Adder Design in CNTFET Technology

This letter presents an efficient multiternary digit (trit) adder design in carbon nanotube field effect transistor technology. The adder is based on an efficient single-trit full-adder design with low-complexity encoder and reduced complexity carry-generation unit. Further, we optimize the number of encoder and decoder blocks required while putting together several single-trit full-adder units to realize a multitrit adder. Extensive HSPICE simulation results show roughly 79% reduction in power-delay product for three-trit adders and 88 % reduction in power-delay product for nine-trit adders in comparison to a direct realization.

Low-Complexity Encoder Framework for Window-Level Rate Control Optimization

There exists a tradeoff between visual quality smoothness and buffer constraint in rate control. Thus, a window model and a window-level rate control algorithm were proposed to handle such a tradeoff recently. However, the extra computational complexity and encoding delay were introduced due to a pre-analysis process in the window-level rate control. In this paper, the window model is first improved for a more convenient usage in practical encodings. Second, a new low-complexity encoder framework is proposed based on an open-loop motion estimation (ME). Third, a new memory-efficient algorithm is proposed to eliminate the drift error of open-loop ME. Fourth, the window-level rate control algorithm is improved to adapt to the proposed encoder framework. The experimental results show that the visual quality smoothness is much improved by the proposed algorithm against the traditional one of H.264/AVC. In addition, the improvement of the window-level rate control is better than the previous one in terms of both bit control accuracy and visual quality smoothness. Besides, the proposed encoder could be used to any other rate control algorithms with pre-analysis to reduce computational complexity.

Low-complexity distributed multi-view video coding for wireless video sensor networks based on compressive sensing theory

Sparsity is an attractive feature of images. Images can be efficiently represented using a few significant coefficients and sparse reconstructed from a small set of random linear measurements by utilizing the sparse feature in compressive sensing theory. Storage and transmission of multi-view video sequences involve large volumes of redundant data. These data can be efficiently compressed with techniques which encode the signals independently and decode them jointly. By integrating the respective characteristics of compressive sensing and distributed source coding, we propose a novel multi-view video coding approach for use in resource limited devices such as wireless video sensor networks. The proposed approach can explore the sparsity of video images, allow for low complexity encoder and the exploitation of inter-camera correlation without communications among cameras. Simulation results show the proposed framework outperforms the baseline compressive sensing-based scheme of intra frame coding by 3–5dB. Compared with conventional H.264 or DVC scheme, the proposed frameworks simple while the quality of reconstructed image and compressibility are kept.