nils-herrmann/clean_scirepeval huggingface dataset:
Spatial–Spectral Classification of Hyperspectral Images Using Discriminative Dictionary Designed by Learning Vector Quantization

In this paper, a novel discriminative dictionary learning method is proposed for sparse-representation-based classification (SRC) to label highly dimensional hyperspectral imagery (HSI). In SRC, a dictionary is conventionally constructed using all of the training pixels, which is not only inefficient due to the large size of typical HSI images but also ineffective in capturing class-discriminative information crucial for classification. We address the dictionary design problem with the inspiration from the learning vector quantization technique and propose a hinge loss function that is directly related to the classification task as the objective function for dictionary learning. The resulting online learning procedure systematically “pulls” and “pushes” dictionary atoms so that they become better adapted to distinguish between different classes. In addition, the spatial context for a test pixel within its local neighborhood is modeled using a Bayesian graph model and is incorporated with the sparse representation of a single test pixel in a unified probabilistic framework, which enables further refinement of our dictionary to capture the spatial class dependence that complements the spectral information. Experiments on different HSI images demonstrate that the dictionaries optimized using our method can achieve higher classification accuracy with substantially reduced dictionary size than using the whole training set. The proposed method also outperforms existing dictionary learning methods and attains the state-of-the-art results in both the spectral-only and spatial-spectral settings.
A dynamic address-vector quantization algorithm based on inter-block and inter-color correlation for color image coding

The authors extend the A-VQ (address-vector quantization) coding technique to encode RGB (red, green, blue) color images by utilizing the interblock and intercolor correlation. A modified A-VQ technique called dynamic address-vector quantization (DA-VQ) is introduced for encoding of color images. A multilayered address vector quantizer is also introduced, where the code vectors at each layer represent the most probable address combinations of the entries of the address codebook at the previous lower layer. Experimental results show reconstructed images at bit rates of 0.5-0.6 bit per pixel with SNR (signal/noise ratio)=28-31 dB. The resulting bit rate is less than that of a standard VQ coding technique by at least a factor of two.<<ETX>>
A hashing-based scheme for organizing vector quantization codebook

One of the problems in vector quantization (VQ) is its relatively long encoding time especially when an exhaustive search is made for the codevector. This paper presents a hashing-based technique to organize the codebook so that the search time can be significantly reduced. Hashing gives the speed advantages of a direct search, while maintaining a codebook of reasonable size. Experiments show that hashing-based VQ sustained image quality as the encoding time was reduced, while full search VQ suffered greatly. For example, for 2/spl times/2 vectors and with 1024 codebook entries, encoding time was reduced by a factor of 10 without significant loss of image quality.
An interframe dynamic FSVQ codec for video sequence coding

An interframe vector quantization scheme with finite memory called dynamic finite-state vector quantization (DFSVQ) is presented. The encoding of a current input vector consists of a subcodebook, representing the best matchable code vectors, selected from a larger codebook, i.e., a supercodebook. The choice for the entries in the supercodebook is based on the information obtained from the previously encoded blocks where directional conditional block probability matrices are used in the selection of the entries. An adaptive DFSVQ scheme is also proposed in which, when encoding an input vector, first the supercodebook is searched for a matching code vector to satisfy a prespecified waveform distortion. If such a code vector is not found then the whole supercodebook is checked for a better match and a signaling flag along with the corresponding address of the code vector is transmitted to the receiver. The performances of the DFSVQ encoder and its adaptive version are evaluated for several video sequences.<<ETX>>
Object recognition by a Hopfield neural network

A two-dimensional model-based object recognition technique is introduced to identify and locate isolated or overlapping 2-D objects in any position and orientation. A cooperative feature-matching technique is proposed that is implemented by a Hopfield neural network. The proposed matching technique uses the parallelism of the neural network to globally match all the objects in the input scene against all the object models in the model-database at the same time. A global model graph representing all the object models is constructed where each node in the graph represents a feature that has a numerical feature value and is connected to other nodes by an arc representing the relationship or compatibility between them. Object recognition is formulated as matching this global model with an input scene graph representing a single object or several overlapping objects. The performance of the proposed technique is compared with that of a relaxation technique.
Multi-View Automatic Target Recognition using Joint Sparse Representation

We introduce a novel joint sparse representation based multi-view automatic target recognition (ATR) method, which can not only handle multi-view ATR without knowing the pose but also has the advantage of exploiting the correlations among the multiple views of the same physical target for a single joint recognition decision. Extensive experiments have been carried out on moving and stationary target acquisition and recognition (MSTAR) public database to evaluate the proposed method compared with several state-of-the-art methods such as linear support vector machine (SVM), kernel SVM, as well as a sparse representation based classifier (SRC). Experimental results demonstrate that the proposed joint sparse representation ATR method is very effective and performs robustly under variations such as multiple joint views, depression, azimuth angles, target articulations, as well as configurations.
Edge-based subband VQ techniques for images and video

A key issue in subband coding is the efficient compression of the less informative but perceptually important upper frequency bands of the decomposed image. A new approach capable of effectively encoding the upper-bands is described. An intra-band vector quantization (VQ) technique is employed for compression of the base-band while the upper frequency bands are encoded by a hierarchical inter-band VQ method. The proposed inter-band vector quantization scheme exploits the redundancies that exist between pels of significant perceptual importance across the upper frequency bands of the same resolution. Such pels are observed to be at or around the edge-locations, displaying similar discontinuity behavior across the subbands. Therefore, an edge-detector is applied on the reconstructed base-band to extract the edge locations, and as a result no overhead information is transmitted to identify the position of these pels in the upper-bands. Furthermore, a residual subband, being the difference between the original base-band of each layer and its encoded version, is incorporated in the proposed inter-band VQ model. Thus, the proposed interband VQ scheme is simply the vector quantization of pixels across the upper-bands together with their corresponding residual band. Compression results are presented for both digital images and video sequences which demonstrate high subjective visual qualities.
Predictive vector quantizer using constrained optimization

A joint optimization technique is developed for designing the predictor and quantizer of a predictive vector quantizer (PVQ). The proposed technique is based on a constrained optimization technique that makes use of a Lagrangian formulation and iteratively solves the Lagrangian error function to obtain a locally optimal solution for the predictor and quantizer. Simulation results show that the proposed PVQ design outperforms the conventional PVQ schemes, such as the closed-loop design and the jointly-optimized technique.<<ETX>>
Advances in residual vector quantization: a review

Advances in residual vector quantization (RVQ) are surveyed. Definitions of joint encoder optimality and joint decoder optimality are discussed. Design techniques for RVQs with large numbers of stages and generally different encoder and decoder codebooks are elaborated and extended. Fixed-rate RVQs, and variable-rate RVQs that employ entropy coding are examined. Predictive and finite state RVQs designed and integrated into neural-network based source coding structures are revisited. Successive approximation RVQs that achieve embedded and refinable coding are reviewed. A new type of successive approximation RVQ that varies the instantaneous block rate by using different numbers of stages on different blocks is introduced and applied to image waveforms, and a scalar version of the new residual quantizer is applied to image subbands in an embedded wavelet transform coding system.
Finite-state residual vector quantization using a tree-structured competitive neural network

Finite-state vector quantization (FSVQ) is known to give better performance than the memoryless vector quantization (VQ). This paper presents a new FSVQ scheme, called finite-state residual vector quantization (FSRVQ), in which each state uses a residual vector quantizer (RVQ) to encode the input vector. This scheme differs from the conventional FSVQ in that the state-RVQ codebooks encode the residual vectors instead of the original vectors. A neural network predictor estimates the current block based on the four previously encoded blocks. The predicted vector is then used to identify the current state as well as to generate a residual vector (the difference between the current vector and the predicted vector). This residual vector is encoded using the current state-RVQ codebooks. A major task in designing our proposed FSRVQ is the joint optimization of the next-state codebook and the state-RVQ codebooks. This is achieved by introducing a novel tree-structured competitive neural network in which the first layer implements the next-state function, and each branch of the tree implements the corresponding state-RVQ. A joint training algorithm is also developed that mutually optimizes the next-state and the state-RVQ codebooks for the proposed FSBVQ. Joint optimization of the next-state function and the state-RVQ codebooks eliminates a large number of redundant states in the conventional FSVQ design; consequently, the memory requirements are substantially reduced in the proposed FSRVQ scheme. The proposed FSRVQ can be designed for high bit rates due to its very low memory requirements and the low search complexity of the state-RVQ's. Simulation results show that the proposed FSRVQ scheme outperforms conventional FSVQ schemes both in terms of memory requirements and the visual quality of the reconstructed image. The proposed FSRVQ scheme also outperforms JPEG (the current standard for still image compression) at low bit rates.
Realizing Super-Resolution with Superimposed Projection

We consider the problem of rendering high-resolution images on a display composed of multiple superimposed lower-resolution projectors. A theoretical analysis of this problem in the literature previously concluded that the multi-projector superimposition of low resolution projectors cannot produce high resolution images. In our recent work, we showed to the contrary that super-resolution via multiple superimposed projectors is indeed theoretically achievable. This paper derives practical algorithms for real multi-projector systems that account for the intra- and inter-projector variations and that render high-quality, high-resolution content at real-time interactive frame rates. A camera is used to estimate the geometric, photometric, and color properties of each component projector in a calibration step. Given this parameter information, we demonstrate novel methods for efficiently generating optimal sub-frames so that the resulting projected image is as close as possible to the given high resolution images.
Analysis of green noise error diffusion

Unlike blue-noise halftone patterns which are composed of isolated pixels, green noise patterns are composed of pixel-clusters. This makes green noise halftones robust to printer distortions due to the poor isolated pixel reproduction. Levien modified error diffusion by adding a hysteresis filter that feeds a filtered version of the past outputs into the quantizer input in classic error diffusion in order to promote dot-clustering. The patterns produced by the Levien algorithm were analyzed by Lau, Arce and Galagher who coined the term “green-noise” since the pattern power spectrum was mid-frequency rather than high-frequency (characteristic of “blue-noise” halftones). However no work has been done to study Leviens algorithm directly in the frequency domain and understand why it works as a green-noise generator. In this paper we will first analyze the Levien green noise error diffusion based on the gain model proposed by Kite et al.. We isolate the noise shaping behavior of the Levien algorithm in the frequency domain and show that it indeed shapes noise into the mid-frequency regions. We then develop two new green noise generators from frequency domain considerations by first by modifying how the error term is computed in conventional error diffusion and then by replacing the error filter in conventional error diffusion by an infinite impulse response (IIR) filter.
Hardcopy image barcodes via block-error diffusion

Error diffusion halftoning is a popular method of producing frequency modulated (FM) halftones for printing and display. FM halftoning fixes the dot size (e.g., to one pixel in conventional error diffusion) and varies the dot frequency according to the intensity of the original grayscale image. We generalize error diffusion to produce FM halftones with user-controlled dot size and shape by using block quantization and block filtering. As a key application, we show how block-error diffusion may be applied to embed information in hardcopy using dot shape modulation. We enable the encoding and subsequent decoding of information embedded in the hardcopy version of continuous-tone base images. The encoding-decoding process is modeled by robust data transmission through a noisy print-scan channel that is explicitly modeled. We refer to the encoded printed version as an image barcode due to its high information capacity that differentiates it from common hardcopy watermarks. The encoding/halftoning strategy is based on a modified version of block-error diffusion. Encoder stability, image quality versus information capacity tradeoffs, and decoding issues with and without explicit knowledge of the base image are discussed.
On the Resolution Limits of Superimposed Projection

Multi-projector super-resolution is the dual of multi-camera super-resolution. The goal of projector super-resolution is to produce a high resolution frame via superimposition of multiple low resolution subframes. Prior work claims that it is impossible to improve resolution via superimposed projection except in specialized circumstances. Rigorous analysis has been previously restricted to the special case of uniform display sampling, which reduces the problem to a simple shift-invariant deblurring. To understand the true behavior of superimposed projection as an inverse of classical camera super-resolution, one must consider the effects of non-uniform displacements between component subframes. In this paper, we resolve two fundamental theoretical questions concerning resolution enhancement via superimposed projection. First, we show that it is possible to reproduce frequencies that are well beyond the Nyquist limit of any of the component subframes. Second, we show that nonuniform sampling and pixel reconstruction functions impose fundamental limits on achievable resolution.
A high quality, fast inverse halftoning algorithm for error diffused halftones

We present an inverse halftoning algorithm for error diffused halftones. At each pixel, the algorithm applies a separable 7/spl times/7 FIR filter parameterized by the computed local horizontal and vertical gradients. All operations are entirely local; only 7 rows of image storage and fewer than 300 operations per pixel are required. The algorithm can be easily implemented in embedded software or hardware. We compare our algorithm with previously reported approaches, and show that it delivers comparable PSNR and subjective quality at a fraction of the computation and memory requirements. A C implementation of the algorithm is available.
Design of optimum multi-dimensional energy compaction filters

We discuss the design of optimum signal-adapted multi-dimensional energy compaction filters. As in the one-dimensional (1-D) case, the energy compaction problem is linear in the auto-correlation coefficients of the compaction filter, which must also satisfy the multi-dimensional (m-D) equivalent of the Nyquist-(M) condition. If a minimum-phase spectral factor exists the optimum compaction filter is recovered using the m-D Discrete Hilbert Transform (DHT). If a minimum phase spectral factor does not exist, an iterative algorithm based on multi-objective goal attainment is proposed. The Nyquist-M condition is enforced while simultaneously forcing the autocorrelation coefficients of the compaction filter to be as close as possible to the coefficients of the product filter and the compaction gain of the optimum compaction filter to be close to the compaction gain produced by using the optimum product filter.
Adaptive threshold modulation for error diffusion halftoning

Grayscale digital image halftoning quantizes each pixel to one bit. In error diffusion halftoning, the quantization error at each pixel is filtered and fed back to the input in order to diffuse the quantization error among the neighboring grayscale pixels. Error diffusion introduces nonlinear distortion (directional artifacts), linear distortion (sharpening), and additive noise. Threshold modulation, which alters the quantizer input, has been previously used to reduce either directional artifacts or linear distortion. This paper presents an adaptive threshold modulation framework to improve halftone quality by optimizing error diffusion parameters in the least squares sense. The framework models the quantizer implicitly, so a wide variety of quantizers may be used. Based on the framework, we derive adaptive algorithms to optimize 1) edge enhancement halftoning and 2) green noise halftoning. In edge enhancement halftoning, we minimize linear distortion by controlling the sharpening control parameter. We may also break up directional artifacts by replacing the thresholding quantizer with a deterministic bit flipping (DBF) quantizer. For green noise halftoning, we optimize the hysteresis coefficients.
Fast blind inverse halftoning

We present a fast, non-iterative technique for producing grayscale images from error diffused and dithered halftones. The first stage of the algorithm consists of a Gaussian filter and a median filter, while the second stage consists of a bandpass filter, a thresholding operation, and a median filter. The second stage enhances the rendering of edges in the inverse halftone. We compare our algorithm to the best reported statistical smoothing, wavelet, and Bayesian algorithms to show that it delivers comparable PSNR and subjective quality at a fraction of the computation and memory requirements. For error diffused halftones, our technique is seven times faster than the MAP estimation method and 75 times faster than the wavelet method. For dithered halftones, our technique is 200 times faster than the MAP estimation method. A C implementation of the algorithm is available.
Optimal design of real and complex minimum phase digital FIR filters

We present a generalized optimal minimum phase digital FIR filter design algorithm that supports (1) arbitrary magnitude response specifications, (2) high coefficient accuracy, and (3) real and complex filters. The algorithm uses the discrete Hilbert transform relationship between the magnitude spectrum of a causal real sequence and its minimum phase delay phase spectrum given by Cizek (1970). We extend the transform pair to the complex case and show that the algorithm gives arbitrary coefficient accuracy. We present design examples that exceed the coefficient accuracy of the optimal real minimum phase filters reported by Chen and Parks (1986) and reduce the length of the optimal complex linear phase filters designed by Karam and McClellan (1995).
Design and analysis of vector color error diffusion halftoning systems

Traditional error diffusion halftoning is a high quality method for producing binary images from digital grayscale images. Error diffusion shapes the quantization noise power into the high frequency regions where the human eye is the least sensitive. Error diffusion may be extended to color images by using error filters with matrix-valued coefficients to take into account the correlation among color planes. For vector color error diffusion, we propose three contributions. First, we analyze vector color error diffusion based on a new matrix gain model for the quantizer, which linearizes vector error diffusion. The model predicts the key characteristics of color error diffusion, esp. image sharpening and noise shaping. The proposed model includes linear gain models for the quantizer by Ardalan and Paulos (1987) and by Kite et al. (1997) as special cases. Second, based on our model, we optimize the noise shaping behavior of color error diffusion by designing error filters that are optimum with respect to any given linear spatially-invariant model of the human visual system. Our approach allows the error filter to have matrix-valued coefficients and diffuse quantization error across color channels in an opponent color representation. Thus, the noise is shaped into frequency regions of reduced human color sensitivity. To obtain the optimal filter, we derive a matrix version of the Yule-Walker equations which we solve by using a gradient descent algorithm. Finally, we show that the vector error filter has a parallel implementation as a polyphase filterbank.
Stabilization of 2-D recursive digital filters by the DHT method

In this paper, we discuss the discrete Hilbert transform (DHT) method of stabilizing unstable two-dimensional (2-D) recursive digital filters originally proposed by Read and Treitel (se  1973). We show that even in the one-dimensional case, the DHT method may yield an unstable polynomial when the given unstable polynomial has zeros on the unit circle. This is the case in the example presented by Read and Treitel, where the given 2-D polynomial has zeros on the unit bicircle. We show that the DHT method cannot guarantee stability if the unstable 2-D polynomial has zeros on the unit bicircle.
Parallel implementation of multifilters

A multifilter is a filter with matrix-valued coefficients, and is used in the processing of vector-valued signals, e.g. color images. Convolution becomes a vector sum of matrix-vector multiplication. In this paper, we efficiently implement a multifilter as a parallel combination of scalar filters. Each scalar filter works on one component of the input vector signal, which increases processing speed by the dimension of the vector-valued signal. This means that by using N processors, the throughput is increased by a factor of N while the total memory usage remains unchanged. We also present a frequency-domain analysis of the filtering.