nils-herrmann/clean_scirepeval huggingface dataset:
Conditional entropy coding of VQ indexes for image compression

Vector quantization (VQ) is a source coding methodology with provable rate-distortion optimality. However, despite more than two decades of intensive research, VQ theoretical promise is yet to be fully realized in image compression practice. Restricted by high VQ complexity in dimensions and due to high-order sample correlations in images, block sizes of practical VQ image coders are hardly large enough to achieve the rate-distortion optimality. Among the large number of VQ variants in the literature, a technique called address VQ (A-VQ) by Nasrabadi and Feng (1990) achieved the best rate-distortion performance so far to the best of our knowledge. The essence of A-VQ is to effectively increase VQ dimensions by a lossless coding of a group of 16-dimensional VQ codewords that are spatially adjacent. From a different perspective, we can consider a signal source that is coded by memoryless basic VQ to be just another signal source whose samples are the indices of the memoryless VQ codewords, and then induce the problem of lossless compression of the VQ-coded source. If the memoryless VQ is not rate-distortion optimal (often the case in practice), then there must exist hidden structures between the samples of VQ-coded source (VQ codewords). Therefore, an alternative way of approaching the rate-distortion optimality is to model and utilize these inter-codewords structures or correlations by context modeling and conditional entropy coding of VQ indexes.
Double-step generation of ellipses

The principle of double-step incremental generation of nonparametric curves on a discrete plane is used to develop a double-step algorithm for scan converting ellipses in sixteen separate segments. The algorithm iterates only half as many times as current algorithms, while each iteration demands the same number of operations or slightly fewer operations than M. Pitteway's (1967, 1985) algorithm (previously the fastest one). This marked gain in the speed of scan conversion is due to the fact that the middle pixel in a two-step increment is obtained without computation.<<ETX>>
Efficient Multiple-Description Image Coding Using Directional Lifting-Based Transform

This paper proposes an efficient two-description image coding technique. The two side descriptions of an image are generated by quincunx subsampling. The decoding from any side description is done by an interpolation process that exploits sample correlation. Although the quincunx subsampling is a natural choice for the best use of sample correlations in image multiple-description coding (MDC), each side description is not amenable to existing image coding techniques because the pixels are not aligned rectilinearly. We show how this difficulty can be overcome by an adaptive directional lifting (ADL) transform that is particularly suitable for decorrelating samples on the quincunx lattice. The ADL transform can be embedded into JPEG 2000 to construct a practical MD image encoder. Experimental results demonstrate that the proposed image MDC scheme can achieve good coding performance.
Fovea weighting of multiview computational displays for enhanced user experience

A challenge for multiview displays is how to concurrently exhibit many different views on the same physical medium without sacrificing image quality for individual viewers. To address the above challenge, we propose a novel scheme of fovea weighting in the framework of the TPVM multiview computational display to enhance users' visual experiences. Underlying our new design is an optimization problem of nonnegative matrix factorization. This seemingly difficult problem turns out to be solvable by efficient algorithms. The effectiveness of the proposed multiview fovea weighting is validated by simulation results.
Joint UEP and layered source coding with application to transmission of JPEG-2000 coded images

This paper presents a joint source-channel coding framework based on layered source coding and Reed-Solomon channel coding for unequal error protection. An iterative procedure is described to search for the best source coding rate and the optimal UEP of layered bitstreams. We apply our JSCC technique to transmission of JPEG-2000 coded images over binary symmetric channels. Compared to results reported in the literature, our UEP based approach gives better results while having lower complexity.
Error Resilient Multiple Description Compression of Vector Graphics

This research is motivated by the needs of robust streaming of vector graphics contents over the Internet, wireless and other lossy networks. We present a multiple description coding (MDC) technique for error resilient compression and transmission of 2D vector graphics contents. An object is coded into two or more so-called co-descriptors, which are transmitted in separate data packets and generally via different network routes from a server to a client. Each co-descriptor can autonomously provide an approximation of the input object, and it can collaborate with other co-descriptors, if also available at the decoder, to refine the approximation
Hybrid image compression scheme based on wavelet transform and adaptive context modeling

We propose a hybrid image compression scheme based on wavelet transform, HVS thresholding and L/sub /spl infin//-constrained adaptive context modelling. This hybrid system combines the strengths of the wavelet transform, the HVS thresholding and the adaptive context modelling to result in a near optimal compression scheme. The wavelet transform is very powerful in localizing the global spatial and frequency correlation. The HVS model-based thresholding is designed to exploit and eliminate the wavelet coefficients insensitive to the human visual system. The context-based modelling is superior in decorrelating the local redundancy. In the scheme, the image is first decomposed into the multiresolution subimages using the orthogonal wavelet transform; each subimage corresponds to a octave band in the wavelet decomposition. The coefficients in the high-pass octave bands of the wavelet transform are then quantized through HVS frequency- and spatial model-based thresholding and vector quantization into wavelet decomposition with only significant coefficients to the HVS retained. In this HVS quantized wavelet decomposition, the coefficients insignificant to the human visual system are normalized to zero and the global spatial and frequency correlation are exploited and removed. Then the quantized subimages in the low-pass band and the remaining high-pass octave bands of each octave level are processed using the L/sub /spl infin//-constrained CALIC to de-correlate the local redundancy. It is demonstrated that the hybrid scheme is one of the best compression schemes in achieving the excellent compression rates and competitive PSNR while maintaining a small visual distortion. In comparing with the original CALIC, we were able to increase the PSNR by 0.65 dB or more and obtain bit rates 15 percent lower than the latter. We were also able to obtain competitive PSNR results against the best wavelet coders, while maintaining a smaller visual distortion. In particular, the wavelet CALIC was able to obtain 1.34 to 7.84 dB higher PSNR on the standard ISO test benchmarks than the SPIHT, one of the best wavelet coder.
Low bit-rate image coding via local random down-sampling

A common practice in low bit-rate image/video compression is uniform spatial down-sampling at the encoder and upsampling at the decoder. The down-sampling is performed in conjunction with deterministic low-pass filtering (e.g., Gaussian or the alike) to prevent aliasing. The down-sampled image is compressed and decompressed as usual; the upsampling is treated as an image restoration problem. In this paper, we show that the rate-distortion performance of the above low bit-rate image coding system can be improved, if the deterministic low-pass down-sampling filter is replaced by a random convolution kernel. The resulting down-sampled image is a two-dimensional array of local random measurements; this smaller image is still compressible in most cases. Accordingly, the decoder recovers the image from these local random measurements in the framework of compressive sensing. Theoretical analysis is conducted to support the superior performance of the proposed new method over its predecessors, and it is corroborated by our simulation results. At low to medium bit rates, the new method outperforms not only JPEG 2000 but also our earlier low bit-rate image codec CADU, with clear advantages over the competing methods in the reconstruction of high frequency features. In addition, the new method retains the system advantages of low encoder complexity and standard compliance as in CADU.
Layered Multicast with Inter-Layer Network Coding

Multirate multicast is a powerful methodology of multimedia communication in heterogenous networks. A vari- ant of multirate multicast motivated by scalable multimedia streaming is layered multicast, where the transmitted signal is presented in successive data layers. With recent advances of network coding theory, many layered multicast schemes using network coding have been proposed to improve the performance of traditional routing based layered multicast. They divide the network into different layers and construct a unirate multicast network code for each layer. However, these schemes do not perform network coding between data layers, and consequently cannot realize the full potential of network coding. In this paper, we propose a novel approach to layered multicast that allows network coding of data in different layers. This relaxation lends the proposed scheme greater flexibility in optimizing the data flow than previous layered solutions, and thus achieves higher throughput.
Cooperative simultaneous localization and mapping for multi-robot: Approach & experimental validation

This paper focuses the multi-robot cooperative simultaneous localization and mapping (SLAM) problem. First, we proposed a decoupled fashion of the map building. Through this fashion, each robot builds the local sub-map independently. The sub-map fusion approach based on Extended Kalman Filter (EKF) is provided. Second, we implemented the active motion of each robot during the local sub-map building process. Each robot will choose the optimized control input of the next step. The optimal objective for this paper is to address the accuracy, the information gain, and the multi-robots cooperation. Finally, an experiment system called IIPSLAMPlatform is built to implement the multi-robot cooperative map building. The IIPSLAMPlatform is built in hybrid architecture. The experiments are conducted under this experiment system. We also compared the multi-robot map building with the single robot condition from the view of uncertainty. The result shows the advantage of multi-robot map building.
Standard-compliant multiple description image coding by spatial multiplexing and constrained least-squares restoration

We propose a practical standard-compliant multiple description (MD) image coding technique. Multiple descriptions of an image are generated in the spatial domain by an adaptive prefiltering and uniform down sampling process. The resulting side descriptions are conventional square sample grids that are interleaved with one the other. As such each side description can be coded by any of the existing image compression standards. A side decoder reconstructs the input image by first decompressing the down-sampled image and then solving a least-squares inverse problem, guided by a two-dimensional windowed piecewise autoregressive model. The central decoder is algorithmically similar to the side decoder, but it improves the reconstruction quality by using received side descriptions as additional constraints when solving the underlying inverse problem. Compared with its predecessors the proposed image MD technique offers the lowest encoder complexity, complete standard compliance, competitive rate-distortion performance, and superior subjective quality.
Context quantization with Fisher discriminant for adaptive embedded wavelet image coding

Recent progress in context modeling and adaptive entropy coding of wavelet coefficients has probably been the most important catalyst for the rapidly maturing area of wavelet image compression technology. In this paper we identify statistical context modeling of wavelet coefficients as the determining factor of rate-distortion performance of wavelet codecs. We propose a new context quantization algorithm for minimum conditional entropy. The algorithm is a dynamic programming process guided by Fisher's linear discriminant. It facilitates high-order context modeling and adaptive entropy coding of embedded wavelet bit streams, and leads to superb compression performance in both lossy and lossless cases.
ℓ2 Optimized Predictive Image Coding With ℓ∞ Bound

In many scientific, medical, and defense applications of image/video compression, an l∞ error bound is required. However, pure l∞-optimized image coding, colloquially known as near-lossless image coding, is prone to structured errors such as contours and speckles if the bit rate is not sufficiently high; moreover, most of the previous l∞-based image coding methods suffer from poor rate control. In contrast, the l2 error metric aims for average fidelity and hence preserves the subtlety of smooth waveforms better than the l∞ error metric and it offers fine granularity in rate control, but pure l2-based image coding methods (e.g., JPEG 2000) cannot bound individual errors as the l∞-based methods can. This paper presents a new compression approach to retain the benefits and circumvent the pitfalls of the two error metrics. A common approach of near-lossless image coding is to embed into a DPCM prediction loop a uniform scalar quantizer of residual errors. The said uniform scalar quantizer is replaced, in the proposed new approach, by a set of context-based l2-optimized quantizers. The optimization criterion is to minimize a weighted sum of the l2 distortion and the entropy while maintaining a strict l∞ error bound. The resulting method obtains good rate-distortion performance in both l2 and l∞ metrics and also increases the rate granularity. Compared with JPEG 2000, the new method not only guarantees lower l∞ error for all bit rates, but also it achieves higher PSNR for relatively high bit rates.
Segmentation-based progressive image coding

The authors present, as an image pyramid, an adaptive, tree-structured segmentation to facilitate progressive transmission. The new image pyramid is semantically more powerful than regular tessellations while syntactically simpler than free segmentation. This good compromise between adaptability and complexity is a key to the high compression ratios achieved by the proposed image coder. The performance is further enhanced by exploiting the statistical dependency between layers of the image pyramid.<<ETX>>
Index Assignment Optimization for Joint Source-Channel MAP Decoding

Channel-optimized quantizer index assignment and maximum a posteriori (MAP) decoding have been extensively studied for error-resilient communications. An interesting and largely untreated problem is how to optimize the index assignment with respect to joint source-channel MAP decoding. In this paper we formulate the above problem as one of quadratic programming, and discuss its solutions from very general to some special cases. Experimental results are presented to demonstrate the performance improvement of the index assignments optimized for MAP decoding over those designed for hard-decision decoding.
Information Flows in Video Coding

We study information theoretical performance of common video coding methodologies at the frame level. Via an abstraction of consecutive video frames as correlated random variables, many existing video coding techniques, including the baseline of MPEG-x and H.26x, the scalable coding and the distributed video coding, can have corresponding information theoretical models. The theoretical achievable rate distortion regions have been completely solved for some systems while for others remain open. We show that the achievable rate region of sequential coding equals to that of predictive coding for Markov sources. We give a theoretical analysis of the coding efficiency of B frames in the popular hybrid video coding architecture, bringing new understanding of the current practice. We also find that distributed sequential video coding generally incurs a performance loss if the source is not Markov.
Optimal Media Communication in Capacity Constrained Networks

For a network with links of finite capacity, we formulate the problem of optimal multimedia communication in its multicast settings. The goal is to find source coding and network communication schemes to communicate a real valued source to a number of sinks to realize the best reconstruction quality at all the sink nodes. We find that the possible reconstruction distortions at the sinks are entangled in a highly non-trivial way. An intriguing problem is that of finding the set of achievable distortions at the sink nodes. Equivalently, we formulate the problem as that of finding a coding scheme that minimizes a weighted sum of the distortions. We realize that separate source coding and network communication approaches are sub-optimal in general. We then devise a powerful strategy based on optimal routing of balanced multiple description codes, for which we develop polynomial time sequential and iterative algorithms. Finally, the improvement in the overall quality of source reconstruction by using the proposed schemes is verified through simulations.
Multiresolution modeling and estimation of multisensor data

This paper presents a multiresolution multisensor data fusion scheme for dynamic systems to be observed by several sensors of different resolutions. A state projection equation is introduced to associate the states of a system at each resolution with others. This projection equation together with the state transition equation and the measurement equations at each of the resolutions construct a continuous-time model of the system. The model meets the requirements of Kalman filtering. In discrete time, the state transition is described at the finest resolution and the sampling frequencies of sensors decrease successively by a factor of two in resolution. We can build a discrete model of the system by using a linear projection operator to approximate the state space projection. This discrete model satisfies the requirements of discrete Kalman filtering, which actually offers an optimal estimation algorithm of the system. In time-invariant case, the stability of the Kalman filter is analyzed and a sufficient condition for the filtering stability is given. A Markov-process-based example is given to illustrate and evaluate the proposed scheme of multiresolution modeling and estimation with multiple sensors.
Multispectral image super-resolution with ℓ1,2-norm regularization of spatially-aligned Laplacians

In quest for high spectral fidelity in spatial superresolution of multispectral images, we explore physically-induced, joint spectral-spatial sparsities. The bichromatic image formation model is used to reveal that the discontinuities of a multi-spectral image tend to align spatially across different spectral bands; in other words, the 2D Laplacians of different bands are not only sparse but also agree with one the other in positions of significance. This strong prior of natural images can be incorporated, as an ℓ1,2-norm regularization term, into an inverse problem formulation for superresolution of multispectral images. Experiments show that exploiting the newly discovered joint spectral-spatial sparsities can improve the performance of existing methods, especially in spectral fidelity.
Image Coding on Quincunx Lattice with Adaptive Lifting and Interpolation

Considering that quincunx lattice is a more efficient spatial sampling scheme than square lattice, we investigate a new approach of image coding for quincunx sample arrangement. The key findings are: 1) adaptive directional lifting is particularly suited to decorrelate samples on quincunx lattice, and 2) quincunx samples can be processed by a 2D piecewise autoregressive model to reproduce the image of conventional square pixel grid, while preserving high frequency spatial features well. By incorporating these two techniques into the encoder and decoder respectively, we are able to improve the performance of JPEG 2000 image codec at low to modest bit rates. Since an image can be easily split into quincunx segments, this work has significance for multiple description image/video coding as well
Resynchronization properties of arithmetic coding

Arithmetic coding is a popular and efficient lossless compression technique that maps a sequence of source symbols to an interval of numbers between zero and one. We consider the important problem of decoding an arithmetic code stream when an initial segment of that code stream is unknown. We call decoding under these conditions resynchronizing an arithmetic code. This problem has importance in both error resilience and cryptology. If an initial segment of the code stream is corrupted by channel noise, then the decoder must attempt to determine the original source sequence without full knowledge of the code stream. In this case, the ability to resynchronize helps the decoder to recover from the channel errors. But in the situation of encryption one would like to have very high time complexity for resynchronization. We consider the problem of resynchronizing simple arithmetic codes. This research lays the groundwork for future analysis of arithmetic codes with high-order context models. In order for the decoder to achieve full resynchronization, the unknown, initial b bits of the code stream must be determined exactly. When the source is approximately IID, the search complexity associated with choosing the correct sequence is at least O(2/sup b/2/). Therefore, when b is 100 or more, the time complexity required to achieve full resynchronization is prohibitively high. To partially resynchronize, the decoder must determine the coding interval after b bits have been output by the encoder. For a stationary source and a finite-precision static binary arithmetic coder, the complexity of determining the code interval is O(2/sup 2s/), where the precision is s bits.
Primary-consistent soft-decision color demosaic for digital cameras

Bayer color mosaic sampling scheme is widely used in digital cameras. Given the resolution of CCD sensor arrays, the image quality of digital cameras using Bayer sampling mosaic largely depends on the performance of the color demosaic process. A common and serious weakness shared by all existing color demosaic algorithms is an inconsistency of sample interpolations in different primary color components, which is the culprit for the most objectionable color artifacts. To cure the problem we propose a primary-consistent color demosaic algorithm. The performance of this algorithm is further enhanced by a soft-decision sample interpolation scheme. Experiments demonstrate that the proposed framework of primary-consistent soft-decision color demosaic can significantly improve the image quality of digital cameras.
A simple technique for estimating message lengths for additive noise steganography

We propose a practical steganalysis method to detect hidden information and estimate embedding rate. By modelling steganographic embedding as an additive noise process, we exploit the fact that the mean and variance of stegosignal is a function of embedding rate. This can be used to estimate the embedding rate without knowledge of the original cover object. We present simulation results to demonstrate that the proposed method can estimate embedding rate with reasonable accuracy. The technique we propose is general and can be used for steganalysis of images, video, and audio etc.