nils-herrmann/clean_scirepeval huggingface dataset:
A No-Reference Image Blur Metric Based on the Cumulative Probability of Blur Detection (CPBD)

This paper presents a no-reference image blur metric that is based on the study of human blur perception for varying contrast values. The metric utilizes a probabilistic model to estimate the probability of detecting blur at each edge in the image, and then the information is pooled by computing the cumulative probability of blur detection (CPBD). The performance of the metric is demonstated by comparing it with existing no-reference sharpness/blurriness metrics for various publicly available image databases.
Video Texture and Motion Based Modeling of Rate Variability-Distortion (VD) Curves

We examine and model the bit rate variability-distortion (VD) curve of -4 part 2 variable bit rate (VBR) video encodings. The VD curve has important applications for evaluating the statistical multiplexing of streaming video. We show that the concave VD curve shape at high compression ratios, or equivalently large quantization scales, is influenced by both the texture and the motion information. Based on this insight, we first develop a general VD curve model by analytically expressing the VD curve in terms of elementary statistics (mean, variance, covariance) of the numbers of motion and texture coding bits. In a second step we develop and validate linear and quadratic models for the elementary texture and motion bit statistics, whereby the model parameters are obtained from only two sample encodings. The texture and motion bit models are then employed in our general VD curve model. This work extends our previous work on piecewise models of the VD curve. The texture and motion based VD model obtained from two sample encodings has comparable accuracy to a piecewise model based on three sample encodings. In addition, the texture and motion based VD model provides fundamental insights into how texture and motion affect traffic variability.
Robust image coding using perceptually-tuned channel-optimized trellis-coded quantization

This paper presents a perceptually tuned wavelet-based image coder that is optimized for transmission over the binary symmetric channel (BSC). The proposed coder uses a channel-optimized trellis-coded quantization (COTCQ) stage that is designed to optimize the image coding based on the channel characteristics, without using any channel coding for error protection. A perceptually-tuned rate allocation scheme controls the quantization by tuning the COTCQ quantization stage to the visual masking properties of the wavelet subbands. Examples are presented to illustrate the performance of the proposed image coder.
Efficient Super-Resolution driven by saliency selectivity

This paper presents a low-complexity saliency detector targeted towards efficient selective Super-Resolution (SR). As a result, an improved efficient ATtentive-SELective Perceptual (AT-SELP) framework is presented. The proposed AT-SELP scheme results in a reduced computational complexity for iterative SR algorithms without any perceptible loss in the desired enhanced image/video quality. A perceptually significant set of active pixels is selected for processing by the SR algorithm based on a local contrast sensitivity threshold model and the proposed low complexity saliency detector. Simulation results show that the proposed AT-SELP scheme results in a 15–40% reduction in computational complexity over an efficient Selective Perceptual (SELP) SR scheme without degradation in the visual quality.
Learning signals and systems with Mathematica

Mathematica, a computer algebra system that supports a sophisticated notebook user interface, which is useful for writing tutorials is discussed. Because of this interface, Mathematica was chosen to assist students in learning linear systems theory. Students interact with Mathematica at two different levels. The lower level is a collection of Mathematica routines known as the signal processing packages (SPP). These packages implement symbolic operations such as convolution and linear transforms and graphical operations such as pole zero diagrams and frequency response plots. The higher level of interaction is with a set of tutorial Notebooks on topics such as convolution and the z transform. The complete system will eventually support the theoretical aspects of a signals and systems curriculum from introductory courses through first-year graduate courses.
Selective Error Detection for Error-Resilient Wavelet-Based Image Coding

This paper introduces the concept of a similarity check function for error-resilient multimedia data transmission. The proposed similarity check function provides information about the effects of corrupted data on the quality of the reconstructed image. The degree of data corruption is measured by the similarity check function at the receiver, without explicit knowledge of the original source data. The design of a perceptual similarity check function is presented for wavelet-based coders such as the JPEG2000 standard, and used with a proposed ldquoprogressive similarity-based ARQrdquo (ProS-ARQ) scheme to significantly decrease the retransmission rate of corrupted data while maintaining very good visual quality of images transmitted over noisy channels. Simulation results with JPEG2000-coded images transmitted over the binary symmetric channel, show that the proposed ProS-ARQ scheme significantly reduces the number of retransmissions as compared to conventional ARQ-based schemes. The presented results also show that, for the same number of retransmitted data packets, the proposed ProS-ARQ scheme can achieve significantly higher PSNR and better visual quality as compared to the selective-repeat ARQ scheme.
Trends in multicore DSP platforms

In the last two years, the embedded DSP market has been swept up by the general increase in interest in multicore that has been driven by companies such as Intel and Sun. One reason for this is that there is now a lot of focus on tooling in academia and also a willingness on the part of users to accept new programming paradigms. This industry-wide effort will have an effect on the way multicore DSPs are programmed and perhaps architected. But it is too early to say in what way this will occur. Programming multicore DSPs remains very challenging. The problem of how to take a piece of sequential code and optimally partition it across multiple cores remains unsolved. Hence, there will naturally be a lot of variations in the approaches taken. Equally important is the issue of debugging and visibility. Developing effective and easy-to-use code development and real-time debug tools is tremendously important as the opportunity for bugs goes up significantly when one starts to deal with both time and space. The markets that DSP plays in have unique features in their desire for low power, low cost, and hard real-time processing, with an emphasis on mathematical computation. How well the multicore research being performed presently in academia will address these concerns remains to be seen.
Locally-adaptive perceptual quantization without side information for DCT coefficients

Existing JPEG/MPEG compliant perceptual coding methods do not fully exploit the local variation of perceptual masking thresholds. This work demonstrates that, for natural images with the same perceptual quality, the first-order entropy of the quantizer outputs can be reduced by 15 to 40 percent when optimal locally-adaptive perceptual quantization is used. Locally-adaptive perceptual quantization requires the perceptual thresholds to be available at the decoder. However, transmitting the thresholds is prohibited by the large amount of side information required. In this paper, a DCT-based method is introduced that performs locally-adaptive perceptual quantization without side information based on estimates of the perceptual thresholds from the already quantized data. Since no additional side information is required, this method is compliant with the bit stream syntax of the above mentioned standards.
Chroma coding for video at very low bit rates

Color pictures are usually compressed in a luminance-chrominance coordinate space. We consider the problem of encoding the chrominance information for very low bit rate video coding systems aimed at bit rates in the range 8 to 40 kbps. The challenge is that the chrominance components typically get less than 10 to 20% of the total very low bit rate allocated for the video data. We found that it is sufficient to encode the chrominance information at 1/8 of the luminance resolution in both the horizontal and vertical directions. While, for many of the previous coding methods, the compression is performed independently for the luminance and chrominance coordinates, we propose a coding scheme which exploits the coded luminance data in coding and retrieving the chrominance components. The proposed video coder is an improved extension of an existing luminance-only coder so that color motion video can be coded at very low bit rates under fixed frame and bit rate constraints. It is based on a hybrid waveform coding technique with an implicit model-based component. Very good results were obtained for head-and-shoulders sequences even with chroma rates of less than 7% of the total very low bit rate. In addition, subjective tests indicate that the coded chrominance information improves the visual perception of noisy image features.
Context formation by mutual information maximization

In this paper, the problem of how to form the contexts for context-based entropy coding is studied. The mutual information (MI) between the context and the encoded data is used to measure the context optimality. The MI decreases when contexts are combined together. Given a desired number of contexts, an algorithm is proposed for finding the set of contexts by iteratively combining the pairs that give the minimum MI reduction. The proposed algorithm is applied to form the contexts for the zero coding (ZC) primitive of the JPEG2000 image compression standard. Experimental results show that the number of contexts used as part of the standard can be reduced without loss in the coding performance.
An improved perception-based no-reference objective image sharpness metric using iterative edge refinement

The computation of existing sharpness/blurriness objective metrics involves measuring the spread of edge pixels in blurred images. However, in blurred images, many edges might go undetected causing the metrics to become inaccurate. In these scenarios, proper recovery of edge pixels can lead to a better correlation between the perceived sharpness and the sharpness metric. This paper presents an iterative edge refinement algorithm. The proposed edge refinement scheme is integrated into a perceptual- based no-reference sharpness metric resulting in an increased correlation with the perceived sharpness and, thus, in an increased sharpness/blurriness prediction accuracy. Results are presented to illustrate the performance of the proposed scheme and metric.
An efficient long-term memory motion compensated prediction scheme and application to error-resilient video transmission

This paper presents a long-term memory motion compensated prediction (MCP) scheme for video sequences based on reusing previously computed motion vectors. The proposed method is similar to the long-term memory MCP scheme introduced by Wiegand et al. (1999) in that multiple frames are involved in the prediction; however, instead of having to store all these frames, only the motion vectors associated with these frames are stored and reused for the prediction of the current frame. As the proposed MCP scheme requires less computations and less bits to represent the motion information compared to existing schemes, it is very suitable to provide error resilience to video coders. Simulation results are presented to illustrate the performance of the proposed scheme as well as its application to provide error-resilience in video transmission.
APIC: adaptive perceptual image coding based on subband decomposition with locally adaptive perceptual weighting

The perceptual subband image coder (PIC) introduced by Safranek and Johnston (1989), selects a noise target level for each subband based on an empirically derived perceptual masking measure. These noise target levels are used to set the quantization level in the DPCM quantizer for every particular subband. It achieves high quality output at bit rates from 0.1 to 0.9 bits/pixel (bpp) depending on the complexity of the image. In this paper, we present an algorithm that locally adapts the quantizer step size at each pixel according to an estimate of the masking measure. This estimate is based on the already coded pixels and predictions of the not yet coded pixels. Compared to the PIC, the proposed method does not require any additional side information. In fact, it eliminates the need to transmit the quantizer step size for each subband. For comparable perceptual quality, the proposed method achieves compression gains up to 40 percent. Typical values are in the order of 20 to 30 percent, depending on the nature of the image. Our algorithm has also better performance for supra-threshold image compression since the perceptual error is distributed more evenly and is not concentrated in the most sensitive regions.
An H.264/SVC memory architecture supporting spatial and course-grained quality scalabilities

The standardized Scalable Video Coding (SVC) extension of H.264/AVC achieves significant improvements in coding efficiency relative to the scalable profiles of prior video coding standards, but its computational complexity and memory access requirements make the design of a low power hardware architecture a challenging task. This paper presents an SVC decoder architecture supporting spatial and coarse-grained quality scalability. The architecture optimizes the size of the on-chip memory and reduces the power-consuming and time-intensive external memory accesses.
Robust hyperspectral image coding with channel-optimized trellis-coded quantization

This paper presents a wavelet-based hyperspectral image coder that is optimized for transmission over the binary symmetric channel (BSC). The proposed coder uses a robust channel-optimized trellis-coded quantization (COTCQ) stage that is designed to optimize the image coding based on the channel characteristics. This optimization is performed only at the level of the source encoder and does not include any channel coding for error protection. The robust nature of the coder increases the security level of the encoded bit stream, and provides a much higher quality decoded image. In the absence of channel noise, the proposed coder is shown to achieve a compression ratio greater than 70:1, with an average peak SNR of the coded hyperspectral sequence exceeding 40 dB. Additionally, the coder is shown to exhibit graceful degradation with increasing channel errors.
No-reference objective wavelet based noise immune image sharpness metric

This paper focuses on no-reference image sharpness/blurriness metrics due to their importance in image, video, and biomedical applications. Simulation results show that existing no-reference objective image sharpness metrics fail to predict correctly the sharpness of images in the presence of noise. A noise-immune wavelet-based sharpness metric is proposed based on the Lipschitz regularity for differentiating between edges and noise singularities. Comparison results reveal the superiority of the proposed method when dealing with a moderate noisy environment.
Automated Detection and Classification of Non-Wet Solder Joints

Non-wet solder joints in processor sockets are causing mother board failures. These board failures can escape to customers resulting in returns and dissatisfaction. The current process to identify these non-wets is to use a 2D or advanced X-ray tool with multidimension capability to image solder joints in processor sockets. The images are then examined by an operator who determines if each individual joint is good or bad. There can be an average of 150 images for an operator to examine for each socket. Each image contains more than 30 joints. These factors make the inspection process time consuming and the output variable depending on the skill and alertness of the operator. This paper presents an automatic defect identification and classification system for the detection of non-wet solder joints. The main components of the proposed system consist of region of interest (ROI) segmentation, feature extraction, reference-free classification, and automatic mapping. The ROI segmentation process is a noise-resilient segmentation method for the joint area. The centroids of the segmented joints (ROIs) are used as feature parameters to detect the suspect joints. The proposed reference-free classification can detect defective joints in the considered images with high accuracy without the need for training data or reference images. An automatic mapping procedure which maps the positions of all joints to a known Master Ball Grid Array file is used to get the precise label and location of the suspect joint for display to the operator and collection of non-wet statistics. The accuracy of the proposed system was determined to be 95.8% based on the examination of 56 sockets (76 496 joints). The false alarm rate is 1.1%. In comparison, the detection rate of a currently available advanced X-ray tool with multidimension capability is in the range of 43% to 75%. The proposed method reduces the operator effort to examine individual images by 89.6% (from looking at 154 images to 16 images) by presenting only images with suspect joints for inspection. When non-wet joints are missed, the presented system has been shown to identify the neighboring joints. This fact provides the operator with the capability to make 100% detection of all non-wets when utilizing a user interface that highlights the suspect joint area. The system works with a 2D X-ray imaging device, which saves cost over more expensive advanced X-ray tools with multidimension capability. The proposed scheme is relatively inexpensive to implement, easy to set up and can work with a variety of 2D X-ray tools.
Distributed video coding with 3D recursive search block matching

This paper presents a novel video compression system with a low complexity encoder. Based on the Slepian-Wolf theorem and the Wyner-Ziv theorem, a video is intraframe coded and interframe decoded in this system. The system performance, the compression rate and the video quality, is affected by the difference between the source information and the side information. To increase the accuracy of the side information, a modified three dimensional recursive search block matching is proposed. The resulting motion vectors preserve the true motion of objects because of the spatial and temporal consistency. The results obtained show a significant improvement over full search block matching algorithm in the term of the rate-distortion measure. The different quantizer design and puncturing table reduce the necessary parity bits for the decoding, and also increase the PSNR. The system performance is also much closer to H.263 interframe coding
Parallel deblocking filter for H.264 AVC/SVC

This paper presents a parallel and scalable solution for adaptive deblocking filtering in H.264/AVC. While traditionally in deblocking filtering, the edges in a macroblock are processed in a sequential order, this paper demonstrates how algorithm modifications can be used to enable processing multiple consecutive edges at the same time. The proposed method increases the throughput in proportion to the number of edges that are being processed simultaneously without affecting the PSNR and bit-rate. Details of the method to process 2 consecutive edges in parallel as well as extensions to process 4 and 8 consecutive edges, are provided. A dedicated hardware architecture to process 2 edges is presented along with synthesis results. The architecture achieves a 2× increase in throughput at the expense of a 2.2× increase in area and a 1.23× increase in power.
Change detection on SAR images by a parametric estimation of the KL-divergence between Gaussian Mixture Models

In the context of multi-temporal synthetic aperture radar (SAR) images for earth monitoring applications, one critical issue is the detection of changes occurring after a natural or anthropic disaster. In this paper, we propose a new similarity measure for automatic change detection using a pair of SAR images acquired at different dates. This measure is based on the evolution of the local statistics of the image between two dates. The local statistics are modeled as a Gaussian Mixture Model (GMM), which approximates the probability density function in the neighborhood of each pixel in the image. The degree of evolution of the local statistics is measured using the Kullback-Leibler (KL) divergence. One analytical expression for approximating the KL divergence between GMMs is given and is compared with the Monte Carlo sampling method. The proposed change detector is compared to the classical mean ratio detector and also to other recent model-based approaches. Tests on the real data show that our detector outperforms previously suggested methods in terms of the rate of missed detections and the total error rates.
Visual attention quality database for benchmarking performance evaluation metrics

With the increased focus on visual attention (VA) in the last decade, a large number of computational visual saliency methods have been developed. These models are evaluated by using performance evaluation metrics that measure how well a predicted map matches eye-tracking data obtained from human observers. Though there are a number of existing performance evaluation metrics, there is no clear consensus on which evaluation metric is the best. This work proposes a subjective study that uses ratings from human observers to evaluate saliency maps computed by existing VA models based on comparing the maps visually with ground-truth maps obtained from eye-tracking data. The subjective ratings are correlated with the scores obtained from existing as well as a proposed objective VA performance evaluation metric using several correlation measures. The correlation results show that the proposed objective VA metric outperforms the existing metrics.
Suppression of Mosquito Noise by Recursive Epsilon-Filters

This work addresses the problem of mosquito noise (MN) reduction in compressed video sequences. A compression-blind approach is adopted; the advantage of such an approach is that it is independent of the particular compressor used and of its particular settings. A recursive filtering scheme is presented. It is shown how the filtering parameter ϵ can be adaptively selected to maximize the denoising performance by minimizing the number of outlier pixels in the filter's support. Simulation results show that the proposed blind MN-denoising scheme outperforms existing MN-denoising methods.
An improved scheme for side information generation in a Distributed Video Coding System

Side information (SI) generation plays a key-role in determining the performance of the Distributed Video Coding System (DVC). Current approaches to DVC rely on motion-compensated interpolation (MCTI) to generate at the decoder the SI which is an estimation of the frame being decoded. This work presents a novel MCTI algorithm. In the proposed scheme, motion estimation utilizes only the low-frequency content of the key frames whereas motion compensation is performed using the entire frequency range. The motivation behind this approach is that MCTI was found to provide better interpolation results in the low frequency range than in the high frequency range. An improved variable-size block matching algorithm (VSBMA) with 3-D recursive search (3DRS) is used for motion estimation. A novel motion-compensated enhancement (MCE) algorithm is also proposed. Simulation results are presented to illustrate the performance of the proposed scheme.