nils-herrmann/clean_scirepeval huggingface dataset:
JPEG2000 encoding with perceptual distortion control

In this paper, a new JPEG2000-compliant encoding approach is proposed to control the JPEG2000 encoding in order to achieve a desired perceptual quality. Our method is based on a vision model that incorporates various masking effects of the human visual perception and on a perceptual distortion metric that takes spatial and spectral summation of individual quantization errors into account. Compared with the conventional JPEG2000 rate-based distortion minimization encoding, our method provides a way to generate consistent quality images with lower bit rate.
Rate-distortion based selective decoding for pixel-domain distributed video coding

This paper presents a rate-distortion based selective decoding for pixel-domain distributed video compression (DVC) system. In the proposed system, the Wyner-Ziv frames are divided into several sub-images. Each of these sub-images is encoded and decoded independently. At the decoder, the rate-distortion ratio is estimated for each bitplane of the sub-images. Only the bitplanes with high rate-distortion ratios are Turbo decoded. A minimum-distance symbol reconstruction is proposed to estimate the rest of the bitplanes by using the side information and Turbo-decoded bitplanes. More accurate placement of the parity bits results in an improved system performance, especially for video sequences with a relatively large static background. Coding results and comparison with existing DVC schemes and with H.264 interframe coding are presented to illustrate the performance of the proposed system.
Distributed Video Coding With Lossy Side Information

The paper presents a distributed video compression (DVC) system with improved rate-distortion performance that is much closer to the performance of H.263 interframe coding as compared to existing DCT-based DVC schemes. The system performance, in terms of compression rate and video quality, is affected by the difference between the source information and the generated side information at the decoder. To improve the accuracy of the side information, a modified three-dimensional recursive search block matching algorithm is proposed. The performance of the proposed DVC system is also investigated when the side information is estimated from lossy video frames that are compressed at different bit rates. Coding results and comparison with existing DVC schemes and with H.263 are presented to illustrate the performance of the proposed system
A Distributed Canny Edge Detector: Algorithm and FPGA Implementation

The Canny edge detector is one of the most widely used edge detection algorithms due to its superior performance. Unfortunately, not only is it computationally more intensive as compared with other edge detection algorithms, but it also has a higher latency because it is based on frame-level statistics. In this paper, we propose a mechanism to implement the Canny algorithm at the block level without any loss in edge detection performance compared with the original frame-level Canny algorithm. Directly applying the original Canny algorithm at the block-level leads to excessive edges in smooth regions and to loss of significant edges in high-detailed regions since the original Canny computes the high and low thresholds based on the frame-level statistics. To solve this problem, we present a distributed Canny edge detection algorithm that adaptively computes the edge detection thresholds based on the block type and the local distribution of the gradients in the image block. In addition, the new algorithm uses a nonuniform gradient magnitude histogram to compute block-based hysteresis thresholds. The resulting block-based algorithm has a significantly reduced latency and can be easily integrated with other block-based image codecs. It is capable of supporting fast edge detection of images and videos with high resolutions, including full-HD since the latency is now a function of the block size instead of the frame size. In addition, quantitative conformance evaluations and subjective tests show that the edge detection performance of the proposed algorithm is better than the original frame-based algorithm, especially when noise is present in the images. Finally, this algorithm is implemented using a 32 computing engine architecture and is synthesized on the Xilinx Virtex-5 FPGA. The synthesized architecture takes only 0.721 ms (including the SRAM READ/WRITE time and the computation time) to detect edges of 512 × 512 images in the USC SIPI database when clocked at 100 MHz and is faster than existing FPGA and GPU implementations.
An Overview of Perceptual Processing for Digital Pictures

This paper presents an overview of state-of-the-art technologies for perceptual processing of digital pictures, as well as a discussion of the issues related to their implementation, optimization and testing. The paper begins with a brief description of the main computational modules that are used as part of a perceptual-based visual signal processing framework. Then, a number of perceptual-based visual processing techniques and applications to which perceptual models are presented, including image/video compression, visual signal quality evaluation, and computer graphics. The most significant research efforts are highlighted for each topic, and a number of issues and views are put forward regarding the related research and opportunities.
Unconstrained ear recognition using deep neural networks

The authors perform unconstrained ear recognition using transfer learning with deep neural networks (DNNs). First, they show how existing DNNs can be used as a feature extractor. The extracted features are used by a shallow classifier to perform ear recognition. Performance can be improved by augmenting the training dataset with small image transformations. Next, they compare the performance of the feature-extraction models with fine-tuned networks. However, because the datasets are limited in size, a fine-tuned network tends to over-fit. They propose a deep learning-based averaging ensemble to reduce the effect of over-fitting. Performance results are provided on unconstrained ear recognition datasets, the AWE and CVLE datasets as well as a combined AWE + CVLE dataset. They show that their ensemble results in the best recognition performance on these datasets as compared to DNN feature-extraction based models and single fine-tuned models.
On the assessment of the quality of textures in visual media

Textures are being extensively used in next-generation video coding, image restoration and computer graphics wherein the stochastic and perceptual properties of textures are exploited. Given the prominence of texture content in image and video processing applications, texture quality assessment and objective quality metrics geared specifically towards texture are of particular importance. This paper outlines an extensive subjective study in which a total of 340 distorted images extracted from 10 reference images were evaluated by a group of 20 human subjects. The subjective Mean Opinion Scores (MOS) thus obtained from the study were used to evaluate several existing mainstream full reference quality metrics with the help of a recently developed quality assessment framework (IVQUEST). Based on the results obtained, it can be concluded that traditional objective metrics do not perform well on texture images and there is a need for specialized texture quality metrics.
Efficient design of digital filters for 2-D and 3-D depth migration

Two- and three-dimensional (2-D and 3-D) depth migration can be performed using 1-D and 2-D extrapolation digital filters, respectively. The depth extrapolation is done, one frequency at a time, by convolving the seismic wavefield with a complex-valued, frequency- and velocity-dependent, digital filter. This process requires the design of a complete set of extrapolation filters: one filter for each possible frequency-velocity pair. Instead of independently designing the frequency- and velocity-dependent filters, an efficient procedure is introduced for designing a complete set of 1-D and 2-D extrapolation filters using transformations. The problem of designing a desired set of migration filters is thus reduced to the design of a single 1-D filter, which is then mapped to produce all the desired 1-D or 2-D migration filters. The new design procedure has the additional advantage that both the 1-D and 2-D migration filters can be realized efficiently and need not have their coefficients precomputed or tabulated.
Reduced-reference quality assessment based on the entropy of DNT coefficients of locally weighted gradients

Reduced-reference (RR) image quality assessment (IQA) methods make use of partial information or features extracted from the reference image for estimating the quality of distorted images. Finding a balance between the number of RR features and image quality estimation accuracy is a difficult task. This paper presents a training-free low cost RRIQA method which requires a very small number of RR features (6 RR features). The proposed RRIQA algorithm is based on the divisive normalization transform (DNT) of locally weighted gradient magnitudes. The weighting of the gradient magnitudes is performed in a locally adaptive manner based on the human visual system's contrast sensitivity and neighborhood gradient information. The RR features are obtained by computing the entropy of each DNT subband and, for each scale, averaging the subband entropies along all orientations, resulting in L RR features (one average entropy per scale) for an L-level DNT. Performance evaluations on four large-scale benchmark databases demonstrates that the proposed RRIQA method delivers highly competitive performance as compared to the state-of-the-art RRIQA models as well as full reference ones.
Prediction of the quality of JPEG-compressed color images based on the SCIELAB metric

The SCIELAB color difference metric has been introduced by Zhang and Wandell (1997) as an improvement of the CIELAB metric for predicting perceptual differences among color images. Although SCIELAB-based image quality models have been proposed for predicting image compression distortions, Zhang and Wandell indicated that SCIELAB performs poorly when predicting JPEG-DCT compressed image distortions. This paper presents an algorithm for predicting the quality for JPEG-DCT compressed color images based on the SCIELAB metric distribution. Results are presented to illustrate the performance of the proposed algorithm.
Locally-adaptive image coding based on a perceptual target distortion

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 uses a locally-adaptive perceptual quantization scheme based on a tractable perceptual distortion metric. Our strategy is to exploit human visual masking properties by deriving visual masking thresholds in a locally-adaptive fashion. The derived masking thresholds are used in controlling the quantization stage by adapting the quantizer reconstruction levels in order to meet a desired target perceptual distortion. The proposed coding scheme is flexible in that it works with any subband-based decomposition and with block-based transform methods. Compared to the existing perceptual transform-based and block-based methods, the proposed perceptual coding method exhibits superior performance in terms of the bit rate and distortion control. Coding results are presented to illustrate the performance of the presented coding scheme.
Mutual information-based analysis of JPEG2000 contexts

Context-based arithmetic coding has been widely adopted in image and video compression and is a key component of the new JPEG2000 image compression standard. In this paper, the contexts used in JPEG2000 are analyzed using the mutual information, which is closely related to the compression performance. We first show that, when combining the contexts, the mutual information between the contexts and the encoded data will decrease unless the conditional probability distributions of the combined contexts are the same. Given I, the initial number of contexts, and F, the final desired number of contexts, there are S(I,F) possible context classification schemes where S(I,F) is called the Stirling number of the second kind. The optimal classification scheme is the one that gives the maximum mutual information. Instead of using an exhaustive search, the optimal classification scheme can be obtained through a modified generalized Lloyd algorithm with the relative entropy as the distortion metric. For binary arithmetic coding, the search complexity can be reduced by using dynamic programming. Our experimental results show that the JPEG2000 contexts capture the correlations among the wavelet coefficients very well. At the same time, the number of contexts used as part of the standard can be reduced without loss in the coding performance.
Increasing retention through Introduction to Engineering Design

This paper describes the laboratory component of the Introduction to Engineering freshman course offered in the School of Electrical, Computer & Energy Engineering (ECEE) at Arizona State University. The lab component was designed to excite the students about engineering through hands-on lab experiments that expose them to a wide range of areas in electrical and computer engineering including circuits, electronics, communications, digital signal processing, digital image processing, robotics and control. This paper describes the objective, format, and content of the lab and also presents the results of a survey that was given to the students in order to assess the effectiveness of the lab component in increasing retention.
Spatially-Varying Blur Detection Based on Multiscale Fused and Sorted Transform Coefficients of Gradient Magnitudes

The detection of spatially-varying blur without having any information about the blur type is a challenging task. In this paper, we propose a novel effective approach to address this blur detection problem from a single image without requiring any knowledge about the blur type, level, or camera settings. Our approach computes blur detection maps based on a novel High-frequency multiscale Fusion and Sort Transform (HiFST) of gradient magnitudes. The evaluations of the proposed approach on a diverse set of blurry images with different blur types, levels, and contents demonstrate that the proposed algorithm performs favorably against the state-of-the-art methods qualitatively and quantitatively.
Scalable, subband-based video coding with a locally-adaptive perceptual distortion measure

Existing video coders do not fully exploit the local variation of perceptual masking thresholds. This paper presents a locally-adaptive subband-based video coding scheme which is based on a perceptual distortion measure. The local adaption does not require any additional side information since the perceptual thresholds are computed based on the already transmitted data. The proposed coding scheme is scalable in frame rate and spatial resolution. It supports a fixed rate and perceptual target distortion modes. It can be easily generalized to DCT-based methods.
Design of complex multi-dimensional FIR filters by transformation

This paper extends the transformation method by developing two new efficient procedures for the design of complex and real, positive- and negative-symmetric, multi-dimensional filters by transforming even-length 1-D prototype filters with complex (or real) coefficients. The first procedure is used to design complex multi-dimensional filters with a rectangular region of support having odd-length sides, while the second procedure is used for filters with a rectangular region of support having even-length sides. The designed filters can be implemented efficiently using special Chebyshev structures. Design examples are presented to illustrate the performance of the design procedures.
Reduced-delay selective ARQ for low bit-rate image and multimedia data transmission

The paper presents a reduced-delay selective-ARQ scheme based on a similarity check function that measures the degree of corruption in transmitted multimedia data packets. Accordingly, if the packet is found to be badly corrupted based on a specified similarity criterion, it can be considered as a lost packet and retransmitted. The degree of corruption contributed by a particular packet is measured by the proposed similarity check function at the receiver without explicit knowledge of the original source data. Simulation results and comparisons with a conventional ARQ scheme are provided to illustrate the performance of the proposed method.
Error-resilient video coding with channel-optimized trellis-coded quantization

We present an error-resilient channel-optimized coder for the transmission of video over noisy channels. The proposed coder uses a robust 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 only at the level of the source encoder, with no explicit use of channel coding. The robust nature of the coder increases the security level of the encoded bit stream, and eliminates impulsive artifacts induced by channel errors. A novel adaptive classification scheme is employed, which eliminates the need for entropy coding and motion compensation. Consequently, the proposed channel-optimized video coder is especially suitable for wireless video transmission due to its reduced complexity, its robustness to nonstationary signals and channels, and its increased security level. Simulation results are presented to illustrate the performance of the proposed video coder for a wide variety of channel conditions, without the use of error concealment techniques.
A distributed psycho-visually motivated Canny edge detector

This paper proposes a distributed Canny edge detection algorithm which can be mapped onto multi-core architectures for high throughput applications. In contrast to the conventional Canny edge detection algorithm which makes use of the global image gradient histogram to determine the threshold for edge detection, the proposed algorithm adaptively computes the edge detection threshold based on the local distribution of the gradients in the considered image block. The efficacy of the distributed Canny in detecting psycho-visually important edges is validated using a visual sharpness metric. The proposed distributed Canny edge detection algorithm has the capacity to scale up the throughput adaptively, based on the number of computing engines. The algorithm achieves about 72 times speed up for a 16-core architecture, without any change in performance. Furthermore, the internal memory requirements are significantly reduced especially for smaller block sizes. For instance, if a 512×512 image is processed in 64×64 blocks using the proposed scheme, the memory is reduced by a factor of 70 as compared to the original Canny edge detector.
Virtual Bench for Online Real Time Digital Signal Processing Students

Due to the fast growing market for embedded DSP applications, there is a need for electrical engineers with expertise in the field. This paper is concerned with how to expand the real-time DSP course currently offered to on-campus students at Arizona State University so that it can be offered to online students working in industry or enrolled at other academic institutions. In contrast to many courses, the real-time DSP course includes a complementary hardware lab that is necessary for acquiring the programming techniques for mapping algorithms into DSP architectures. In this work, a "virtual bench" is proposed enabling online students to access with ease all available hardware and software tools in the lab via the web using a set of network protocols, power management units, and circuitry for automatic I/O source switching
A multiple exchange Remez algorithm for complex FIR filter design in the Chebyshev sense

The alternation theorem is at the core of the efficient real Chebyshev approximation algorithms. In this paper, the alternation theorem is extended from the real-only to the complex case. A new efficient algorithm is described for designing FIR filters that best approximate in the Chebyshev sense a desired complex-valued function. This algorithm is based on an ascent Remez exchange method applied to a transformation of the complex Chebyshev error, and is basically a generalization of the Parks-McClellan algorithm to the complex case. Numerical examples are presented to illustrate the performance of the proposed algorithm.<<ETX>>
Robust automatic void detection in solder balls

Voids in solder balls can cause board failures. The detection and assessment of voids in solder balls can help in reducing board yield issues caused by incorrect scrapping and rework. X-ray imaging machines make voids visible to the operator for manual inspection. Some existing x-ray inspection systems have void detection algorithms that require the use of intensive manual tuning operations that are time consuming, and inaccurate due to the inability to examine balls overshadowed with other components. In this paper, a robust automatic void detection algorithm is proposed. The proposed method is able to detect voids with different sizes inside the solder balls, including the ones that are overshadowed by board components and under different brightness conditions. Results show that the proposed method achieves a correlation squared in the range of 91% to 97 % with ground truth data from a 3D x-ray scan. The proposed algorithm is fully automated and benefits the manufacturing process by reducing operator effort and by providing a cost effective solution to improve output quality.
Corrections to "Video Texture and Motion Based Modeling of Rate Variability-Distortion (VD) Curves"

In the above-named article, due to a production oversight, mistakes were introduced prior to final printing. "MPEG-4" appeared as "-4" throughout, and should have appeared as "MPEG-4". In [1, p. 645, Table VI], the caption should have read: COMPARISON OF Jmax ESTIMATES BASED ON PIECEWISE APPROXIMATION (P) WITH 2, 3, AND 4 SAMPLES AND COMBINED MODEL (RRMSE (%) VALUES) FOR PERMITTED LOSS PROBABILITIES OF e = 10-7 AND 10-3. In [1, sec. V, p. 645], the last sentence of the first paragraph should have read: "From these considerations, we can already formulate two high-level recommendations for the choice of q1 and q2: (i) there should be a reasonable interval size Δq = q1-q2, and (ii) at least one q should be chosen in the high compression ratio region where the "hump" is typically situated." In [1, p. 647], "-4" should have read as "MPEG-4" in references [7], , and [15]. In reference , "-encoded" should have read "MPEG-encoded." In reference [26], "-2" should have read "MPEG-2." In reference [28], "all-IP? was omitted. In reference [32], "-7" should have read "MPEG-7." The corrected references [7], , , , [26], [28], and [32] are provided.
Camera calibration using adaptive segmentation and ellipse fitting for localizing control points

This paper presents a novel camera calibration method using a circular calibration pattern. The disadvantages and issues with existing state-of-the-art methods are discussed and are overcome in this work. In the proposed iterative method, the captured images of the circular pattern are undistorted and projected onto a fronto parallel plane. The control points are localized in this fronto parallel plane using a novel approach for more accurately localizing the control points in the images based on adaptive segmentation and ellipse fitting. The localized control points are then projected to their original planes using estimated camera calibration parameters that are refined by minimizing the reprojection error. Simulation results are presented to illustrate the performance of the proposed scheme. These results show that the proposed method reduces the error by up to 57% as compared to the state-of-the-art for high-resolution images, and that the proposed scheme is more robust to blur in the imaged calibration pattern.