nils-herrmann/clean_scirepeval huggingface dataset:
Background recovery from video sequences using motion parameters

This paper presents a novel scheme for extracting a still background occluded by a number of foreground objects, moving in different directions and velocities in a video sequence, such that every background pixel is exposed in at least one of the frames. Each identified foreground object is decomposed into blocks. The proposed scheme is able to efficiently estimate, for each foreground block, a source frame from which the occluded background pixels can be extracted. The pixels of the identified source frames are used to populate the co-located occluded pixels in the initial frame. The efficacy and the simplicity of the algorithm lie in its capacity to recover the background directly from the estimated source frames instead of performing a foreground-background classification for every frame. The proposed algorithm is robust to variations in lighting and is effective in removing both rigid and deformable foreground objects. Simulation results are presented to illustrate the performance of the proposed scheme.
Overview and Traffic Characterization of Coarse-Grain Quality Scalable (CGS) H.264 SVC Encoded Video

The Scalable Video Coding extension (SVC) of the H.264/AVC standard is widely considered for IPTV. SVC supports a variety of scalability modes, including temporal, spatial as well as coarse-grain and medium-grain quality scalabilities. In this paper, we first give an overview of Coarse-Grain quality Scalability (CGS). We generate traces of CGS encodings of long CIF resolution video sequences; the traces provide a simple yet effective characterization of CGS encoded video for performance evaluation of video transport systems, including IPTV systems. We conduct a detailed statistical analysis of the CGS video traces. We compare the bit rate-distortion (RD) and the bit rate variability-distortion (VD) performances of scalable CGS encodings with those of non-scalable SVC single layer encodings. We thus quantify the tradeoff between the rate adaptability afforded by CGS encoding and the cost in terms of RD efficiency compared to non-scalable single-layer video.
Quality Robust Mixtures of Deep Neural Networks

We study deep neural networks for classification of images with quality distortions. Deep network performance on poor quality images can be greatly improved if the network is fine-tuned with distorted data. However, it is difficult for a single fine-tuned network to perform well across multiple distortion types. We propose a mixture of experts-based ensemble method, MixQualNet, that is robust to multiple different types of distortions. The “experts” in our model are trained on a particular type of distortion. The output of the model is a weighted sum of the expert models, where the weights are determined by a separate gating network. The gating network is trained to predict weights for a particular distortion type and level. During testing, the network is blind to the distortion level and type, yet can still assign appropriate weights to the expert models. In order to reduce the computational complexity, we introduce weight sharing into the MixQualNet. We utilize the TreeNet weight sharing architecture as well as introduce the Inverted TreeNet architecture. While both weight sharing architectures reduce memory requirements, our proposed Inverted TreeNet also achieves improved accuracy.
A distributed Canny edge detector and its implementation on FPGA

Edge detection is one of the key stages in image processing and object recognition. The Canny edge detector is one of the most widely-used edge detection algorithms due to its good performance. In this paper, we present a distributed Canny edge detection algorithm that results in significantly reduced memory requirements, decreased latency and increased throughput with no loss in edge detection performance as compared to the original Canny algorithm. The new algorithm uses a low-complexity 8-bin non-uniform gradient magnitude histogram to compute block-based hysteresis thresholds that are used by the Canny edge detector. Furthermore, an FPGA-based hardware architecture of our proposed algorithm is presented in this paper and the architecture is synthesized on the Xilinx Virtex-5 FPGA. Simulation results are presented to illustrate the performance of the proposed distributed Canny edge detector. The FPGA simulation results show that we can process a 512×512 image in 0.287ms at a clock rate of 100 MHz.
Locally adaptive perceptual image coding

Most existing efforts in image and video compression have focused on developing methods to minimize not perceptual but rather mathematically tractable, easy to measure, distortion metrics. While nonperceptual distortion measures were found to be reasonably reliable for higher bit rates (high-quality applications), they do not correlate well with the perceived quality at lower bit rates and they fail to guarantee preservation of important perceptual qualities in the reconstructed images despite the potential for a good signal-to-noise ratio (SNR). This paper presents a perceptual-based image coder, which discriminates between image components based on their perceptual relevance for achieving increased performance in terms of quality and bit rate. The new coder is based on a locally adaptive perceptual quantization scheme for compressing the visual data. Our strategy is to exploit human visual masking properties by deriving visual masking thresholds in a locally adaptive fashion based on a subband decomposition. The derived masking thresholds are used in controlling the quantization stage by adapting the quantizer reconstruction levels to the local amount of masking present at the level of each subband transform coefficient. Compared to the existing non-locally adaptive perceptual quantization methods, the new locally adaptive algorithm exhibits superior performance and does not require additional side information. This is accomplished by estimating the amount of available masking from the already quantized data and linear prediction of the coefficient under consideration. By virtue of the local adaptation, the proposed quantization scheme is able to remove a large amount of perceptually redundant information. Since the algorithm does not require additional side information, it yields a low entropy representation of the image and is well suited for perceptually lossless image compression.
Can the Early Human Visual System Compete with Deep Neural Networks?

We study and compare the human visual system and state-of-the-art deep neural networks on classification of distorted images. Different from previous works, we limit the display time to 100ms to test only the early mechanisms of the human visual system, without allowing time for any eye movements or other higher level processes. Our findings show that the human visual system still outperforms modern deep neural networks under blurry and noisy images. These findings motivate future research into developing more robust deep networks.
Wavelet-based adaptive image denoising with edge preservation

This paper presents a state-of-the-art adaptive wavelet-based denoising method with edge preservation. More specifically, a redundant discrete dyadic wavelet transform (DDWT) is performed on the noisy image to get the wavelet frame decomposition at different scales. Based on the Lip-schitz regularity theory, correlation analysis across scales is performed to detect the significant coefficients from the signal and the insignificant coefficients from the noise for each subband. Different denoising techniques are applied to the significant coefficients and insignificant coefficients separately, based on different statistical models. Unlike most of the existing image denoising methods, the proposed method is able to not only shrink but also increase the magnitude of the noisy wavelet coefficients. Simulation results show that the proposed method has a remarkably superior ability to preserve the edge information and to achieve better visual quality.
Sparse depth estimation using multi-view 3D modeling

This paper presents a 2D to 3D conversion system from multiple views based on the computation of a sparse depth map. This method is able to deal with the multiple views obtained from uncalibrated hand-held cameras without prior knowledge of the camera parameters or scene geometry. The obtained reconstructed sparse depth maps of feature points in 3D scenes provide accurate relative depth information of the objects. Sample ground-truth depth data points are used to calculate a scale factor in order to estimate the true depth by scaling the obtained relative depth using the estimated scale factor. Results are presented to illustrate the performance of the developed system. It is shown that the implemented 2D to 3D conversion system results in a reconstructed depth map that matches the ground-truth depth data.
An Online Web-Based Real Time Digital Signal Processing Course

This work focuses on enabling on-line Web delivery of the real-time DSP course offered at Arizona State University (ASU). For this purpose, tools are developed so that on-line students can directly control, via the Web, the actual DSP boards, hardware equipment, and software tools in the real-time embedded signal processing (RESP) laboratory from remote locations using different networking and I/O protocols. Working online test benches are set up for remote experimentation; two online test benches are presented, one using the Freescale DSP kit, and a second one using the Texas Instruments DSP kit. To test the quality of the on-line real-time DSP delivery and remote access, online assessment was conducted; the obtained feedback reflects high satisfaction recommending the use of the online version
A MATLAB-based framework for image and video quality evaluation

This work provides a MATLAB-based framework for image and video quality evaluation that integrates test content generation, subjective testing interfaces, objective quality metric evaluation and correlation analysis into a single application. It also provides an HTML-based help system and a Graphical User Interface that makes applications based on the framework user friendly. The framework supports a configurable interface that makes it easy to integrate existing or new quality metrics and to compare them against each other. The framework abstracts the user from underlying details like file format, report generation and test flow control.
Multi-Region Texture Image Segmentation Based on Constrained Level-Set Evolution Functions

A multi-region texture image segmentation method based on level-set is proposed in this paper. In the proposed method, each region is represented by one level-set function and these functions evolve simultaneously based on a constraint. The constraint is used to keep a balance between competing regions and to guarantee disjoint and non-overlapping regions. To speed up the curve evolution functions and to prevent them from getting stuck at undesired points, a region competition factor is applied. Edge- and edgeless-based active contours are applied in the proposed method to improve the robustness and the accuracy of the segmentation. The proposed multi-region texture segmentation method is fast and less sensitive to initializations as compared with existing techniques. Different segmentation examples are presented to illustrate the performance of the proposed method.
A motion-augmented super-resolution scheme for very low-bit-rate video enhancement

High compression ratios introduce visible compression artifacts and blurring. To enhance the quality of the compressed video, a super-resolution (SR) scheme is employed. However, the effectiveness of the SR process depends heavily on the amount of subpixel motion that exists between frames in a video sequence, and on the accuracy of the motion vectors (MV). To increase the effectiveness of the SR process, we introduce the concept of added motion prior to video compression. The parameters of this added motion are sent to the receiver with the compressed bit-stream. The proposed SR scheme also exploits existing motion between frames, by estimating MV for each pixel using the original (uncompressed) video. However, since transmitting the pixel-based MV is not practical due to the resulting significant increase in side information, the proposed scheme selects and sends only a very small set of MV based upon their significance in the super-resolution enhancement. The results obtained show a significant improvement in the visual quality of the video sequences, as well as improvement in PSNR.
Select Trends in Image, Video, and Multidimensional Signal Processing [In the Spotlight]

The Image, Video, and Multidimensional Signal Processing (IVMSP) title combines within it a broad scope of technical areas dealing with the representation, processing, analysis, classification, compression, and transmission of still images, video, and general multidimensional data. Within the IEEE Signal Processing Society, the IVMSP Technical Committee (IVMSP-TC) has the charter of promoting and guiding the advancement of these technical areas.
MIGRATION AND PROLIFERATION ANALYSIS FOR BLADDER CANCER CELLS

This paper presents a cell evolution analysis (CEA) scheme for bladder cancer cells. The proposed scheme consists of a cell migration analysis component for computing the overall migration rate of the cell cluster, and a cell proliferation analysis component based on counting the individually segmented cells within the cell cluster at different time points. The proposed CEA scheme performs well for images with poor contrast and high cell concentrations, even when the cells are overlapping and small. Results are presented to illustrate the performance of the proposed scheme
Complex Chebyshev approximation for FIR filter design

The alternation theorem is at the core of efficient real Chebyshev approximation algorithms. In this paper, the alternation theorem is extended from the real-only to the complex case. The complex FIR filter design problem is reformulated so that it clearly satisfies the Haar condition of Chebyshev approximation. An efficient exchange algorithm is derived for designing complex FIR filters in the Chebyshev sense. By transforming the complex error function, the Remez exchange algorithm can be used to compute the optimal complex Chebyshev approximation. The algorithm converges to the optimal solution whenever the complex Chebyshev error alternates; in all other cases, the algorithm converges to the optimal Chebyshev approximation over a subset of the desired bands. The new algorithm is a generalization of the Parks-McClellan algorithm, so that arbitrary magnitude and phase responses can be approximated. Both causal and noncausal filters with complex or real-valued impulse responses can be designed. Numerical examples are presented to illustrate the performance of the proposed algorithm.
Selective Bayesian estimation for efficient super-resolution

In this paper, a new approach to efficient and robust super-resolution is presented. Our method is based on selectively applying a Bayesian MAP estimator to image regions with high spatial activity. The degree of spatial activity is measured using the gradient of the estimated high-resolution image at each iteration. In addition, selective filtering is applied to enhance the visual quality of the estimated high-resolution image. The results obtained via simulation and with real video sequences demonstrate up to a 50% reduction in computational complexity, with improved visual quality, and higher SNR gains for magnification factors of four or more.
A DWT-based perceptually lossless color image compression architecture

In this paper we propose a DWT-based perceptually lossless image compression algorithm and architecture. Many image-capturing devices acquire images in subsampled R-G-B format and have real-time constraints which prohibit conversion to other formats, generally preferred for image compression, such as Y-Cb-Cr. Therefore, we propose a compression algorithm which works performed directly on the subsampled R-G-B data. The scheme exploits the correlation between the color planes and the perceptual sensitivity of the human visual system in order to improve the compression ratio. Moreover it can be easily mapped into an integrated fast architecture yielding very high throughput.
Background subtraction using spatio-temporal continuities

This paper presents a novel scheme for dynamically recovering a background image from consecutive frames of a video sequence based on spatial and temporal continuities. The proposed background subtraction algorithm applies a boundary-level spatial continuity constraint in order to detect and correct ghosting, which corresponds to incorrectly classified foreground regions due to fast moving objects. A pixel-level spatial continuity metric that effectively recovers regions with partial ghosting problems is also proposed in this paper together with a selective application of a temporal continuity metric in order to prevent strong pixel transitions in the background from hindering the background recovery process. The proposed method can also be applied successfully to sequences with deformable foreground objects and non-uniform motion. Simulation results show that the extracted background, when used for foreground detection, results in a higher performance in terms of recall and precision as compared to existing popular schemes.
BitpLAne SelecTive distributed video coding

This paper presents a bitplane selective (BLAST) distributed video coding (DVC) system. In the proposed system, the importance of each bitplane is measured at the decoder, and only the bitplanes with higher importance would be decoded. A minimum-distance symbol reconstruction is proposed to estimate the rest of the bitplanes by using the side information and the decoded bitplanes. The importance is measured with an estimated rate-distortion ratio that makes use of a correlation model for the original source information and the side information. Coding results and comparisons with existing DVC schemes and with H.264 interframe coding are presented to illustrate the performance of the proposed system.
A perceptually-tuned image coder with Channel-Optimized Trellis-Coded Quantization

A system is presented for the compression of digital imagery over binary symmetric channels (BSC). The objective is to minimize the perceived distortion for a desired target bit rate. The proposed system utilizes wavelet decomposition, channel-optimized trellis-coded quantization (COTCQ), and a perceptually-tuned rate allocation scheme to control the quantization. Because of the inherent noise-robust properties of the COTCQ stage, no channel coding is employed. Additionally, the COTCQ design does not utilize entropy coding. Consequently, the proposed image coder is especially suitable for wireless transmission due to its reduced complexity and robustness to channel errors. Examples are presented to illustrate the performance of the proposed image coding system.
Efficient perceptual attentive super-resolution

An efficient Perceptually Attentive (PA) Super-Resolution method is proposed to significantly reduce the computational complexity of iterative super-resolution algorithms without loss of the desired perceptual quality. A perceptually significant constrained set of active pixels is selected for processing by the SR algorithm based on a just noticeable distortion threshold model. These selected active pixels are further reduced by using saliency information that is determined by a visual attention model. Furthermore, the active pixels lying in the attended regions are processed at a higher accuracy by the SR method relative to pixels in other regions. Simulation results are presented to show the preserved desired visual quality and a 30–40% reduction in complexity over a highly efficient Selective Perceptual Fast Two-Step (SELP-FTS) scheme.
Performance of complex Chebyshev approximation in delay-root-Nyquist filter design

Root-Nyquist filters are crucial components in data communications and recently the ability to attach fractional sample delay attributes to them has become very important. Here we explore the application of Chebyshev approximation to design of such filters. Special attention is paid to the use of the Karam-McClellan algorithm, a fully complex extension of the well-established Parks-McClellan algorithm, which delivers peak error performance improvement in the region of one dB or better.
A low-complexity probabilistic genome assembly based on hashing functions with SNP detection

This paper presents an efficient low-complexity genome assembly algorithm with the ability to detect bit errors (SNPs). A hashing function is used to reduce the complexity of the assembly process. The algorithm is tested against genomic sequences of different lengths. Its performance in terms of completeness, accuracy, and efficiency (time and space) is evaluated against Phrap, a well-known sequence assembly tool. It is shown that the proposed assembly algorithm outperforms Phrap in terms of accuracy, time, and memory.
A perceptually tuned embedded zerotree image coder

Embedded zerotree wavelet coding (EZW), introduced by Shapiro (1993) and improved by Said and Pearlman (1996) using an algorithm based on set partitioning in hierarchical trees (SPIHT), is a computationally inexpensive image coding technique and has proven to be very effective at minimizing the mean-square error (MSE) distortion measure. However, minimizing MSE does not guarantee preservation of good perceptual qualities in the decoded image, especially at low bit rates. In this paper we propose a perceptually-tuned embedded zerotree image codec (PEZ) that introduces a perceptual weighting to the wavelet transform coefficients prior to EZW encoding. In this coder the EZW minimizes a perceptually based distortion measure instead of MSE. The perceptual weights for all subbands are computed based on the just noticeable distortion (JND) thresholds for uniform noise. The new perceptually tuned codec has the same complexity as the original EZW/SPIHT techniques and results in a comparable or superior coding performance. Coding results are presented to illustrate the performance of the new coder.
Introduction to Engineering: A Starter's Guide with Hands-On Analog Multimedia Explorations

This lecture provides a hands-on glimpse of the field of electrical engineering. The introduced applications utilize the NI ELVIS hardware and software platform to explore concepts such as circuits, power, analog sensing, and introductory analog signal processing such as signal generation, analog filtering, and audio and music processing. These principals and technologies are introduced in a very practical way and are fundamental to many of the electronic devices we use today. Some examples include photodetection, analog signal (audio, light, temperature) level meter, and analog music equalizer.  Getting Familiar with NI ELVIS / Analog Signal Level Meter Using LEDs / Noise Removal Using Analog Filters / Music Equalizer Using Op-Amps: Volume and Treble Control / Music Composer Using 555 Timers
Error-robust video coding with channel-optimized trellis-coded quantization

This paper presents a low bit rate error-resilient channel-optimized coder for the transmission of video over the binary symmetric channel. The proposed coder uses a channel-optimized trellis-coded quantization (COTCQ) stage that is designed to optimize the video coding based on the channel characteristics. The resilience to channel errors is obtained by optimizing the coder performance only at the level of the source encoder, with no explicit use of channel coding for error protection. Additionally, due to the novel adaptive nature of the coder, the need for entropy coding and motion compensation is eliminated. Simulation results show that our coder provides outstanding quantitative and subjective coding performance for a wide variety of bit error rates, without the use of error concealment techniques.