nils-herrmann/clean_scirepeval huggingface dataset:
Sparse signal reconstruction with multiple side information using adaptive weights for multiview sources

This work considers reconstructing a target signal in a context of distributed sparse sources. We propose an efficient reconstruction algorithm with the aid of other given sources as multiple side information (SI). The proposed algorithm takes advantage of compressive sensing (CS) with SI and adaptive weights by solving a proposed weighted n-ℓ1 minimization. The proposed algorithm computes the adaptive weights in two levels, first each individual intra-SI and then inter-SI weights are iteratively updated at every reconstructed iteration. This two-level optimization leads the proposed reconstruction algorithm with multiple SI using adaptive weights (RAMSIA) to robustly exploit the multiple SIs with different qualities. We experimentally perform our algorithm on generated sparse signals and also correlated feature histograms as multiview sparse sources from a multiview image database. The results show that RAMSIA significantly outperforms both classical CS and CS with single SI, and RAMSIA with higher number of SIs gained more than the one with smaller number of SIs.
Progressive coding of palette images and digital maps

A 2D version of PPM (Prediction by Partial Matching) coding is introduced simply by combining a 2D template with the standard PPM coding scheme. A simple scheme for resolution reduction is given and the 2D PPM scheme extended to resolution progressive coding by placing pixels in a lower resolution image layer. The resolution is increased by a factor of 2 in each step. The 2D PPM coding is applied to palette images and street maps. The sequential results are comparable to PWC. The PPM results are a little better for the palette images with few colors (up to 4-5 bpp) and a little worse for the images with more colors. For street maps the 2D PPM is slightly better. The PPM based resolution progressive coding provides a better result than coding the resolution layers as individual images. Compared to GIF the resolution progressive 2D PPM's coding efficiency is significantly better. An example of combined content-layer/spatial progressive coding is also given.
Video quality assessment and machine learning: Performance and interpretability

In this work we compare a simple and a complex Machine Learning (ML) method used for the purpose of Video Quality Assessment (VQA). The simple ML method chosen is the Elastic Net (EN), which is a regularized linear regression model and easier to interpret. The more complex method chosen is Support Vector Regression (SVR), which has gained popularity in VQA research. Additionally, we present an ML-based feature selection method. Also, it is investigated how well the methods perform when tested on videos from other datasets. Our results show that content-independent cross-validation performance on a single dataset can be misleading and that in the case of very limited training and test data, especially in regards to different content as is the case for many video datasets, a simple ML approach is the better choice.
Distributed coding of multiview sparse sources with joint recovery

In support of applications involving multiview sources in distributed object recognition using lightweight cameras, we propose a new method for the distributed coding of sparse sources as visual descriptor histograms extracted from multiview images. The problem is challenging due to the computational and energy constraints at each camera as well as the limitations regarding inter-camera communication. Our approach addresses these challenges by exploiting the sparsity of the visual descriptor histograms as well as their intra- and inter-camera correlations. Our method couples distributed source coding of the sparse sources with a new joint recovery algorithm that incorporates multiple side information signals, where prior knowledge (low quality) of all the sparse sources is initially sent to exploit their correlations. Experimental evaluation using the histograms of shift-invariant feature transform (SIFT) descriptors extracted from multiview images shows that our method leads to an average bit-rate saving of 30.7% compared to the state-of-the-art distributed compressed sensing method with independent encoding of the sources.
Image dependent energy-constrained local backlight dimming

In this work, we consider and propose two extensions to an optimization-based image dependent backlight dimming algorithm. The first extension introduces error weighting based on human perception of luminance, aiming to improve the perceived image quality; the second extension adds an adjustable term for power consumption to the cost function, allowing flexible power management. Experimental results show that the proposed solution can achieve better results than other algorithms at several power consumption levels.
An Adaptive Multialphabet Arithmetic Coding Based on Generalized Virtual Sliding Window

We propose a novel efficient multialphabet multiplication-free adaptive arithmetic coder. First, we generalize probability estimation via virtual sliding window for the multialphabet case and show that it does not require multiplications and provides a tradeoff between the probability adaptation speed and the precision of the probability estimation. Second, we show how the generalized virtual sliding window can be used to eliminate multiplications and divisions. Finally, we demonstrate that the proposed arithmetic coder provides better compression performance than existing implementations based on state-of-the-art multiplication-free binary arithmetic coders.
Context-based coding of bilevel images enhanced by digital straight line analysis

A new efficient compression scheme for bilevel images containing locally straight edges is presented. This paper is especially focused on lossless (intra) coding of binary shapes for image and video objects, but other images with similar characteristics such as line drawings, layers of digital maps, or segmentation maps are also encoded efficiently. The algorithm is not targeted at document images with text, which can be coded efficiently with dictionary-based techniques as in JBIG2. The scheme is based on a local analysis of the digital straightness of the causal part of the object boundary, which is used in the context definition for arithmetic encoding. Tested on individual images of standard TV resolution binary shapes and the binary layers of a digital map, the proposed algorithm outperforms PWC, JBIG, JBIG2, and MPEG-4 CAE. On the binary shapes, the code lengths are reduced by 21%, 27%, 28%, and 41%, respectively. On the map layers, the reductions are 31%, 34%, 32%, and 64%, respectively. The algorithm is also more efficient on the test material than the state-of-the-art generic bilevel image coder free tree
Efficient Coding of Shape and Transparency for Video Objects

A novel scheme for coding gray-level alpha planes in object-based video is presented. Gray-level alpha planes convey the shape and the transparency information, which are required for smooth composition of video objects. The algorithm proposed is based on the segmentation of the alpha plane in three layers: binary shape layer, opaque layer, and intermediate layer. Thus, the latter two layers replace the single transparency layer of MPEG-4 Part 2. Different encoding schemes are specifically designed for each layer, utilizing cross-layer correlations to reduce the bit rate. First, the binary shape layer is processed by a novel video shape coder. In intra mode, the DSLSC binary image coder presented in is used. This is extended here with an intermode utilizing temporal redundancies in shape image sequences. Then the opaque layer is compressed by a newly designed scheme which models the strong correlation with the binary shape layer by morphological erosion operations. Finally, three solutions are proposed for coding the intermediate layer. The knowledge of the two previously encoded layers is utilized in order to increase compression efficiency. Experimental results are reported demonstrating that the proposed techniques provide substantial bit rate savings coding shape and transparency when compared to the tools adopted in MPEG-4 Part 2.
Reducing the duration of paired-comparison experiments for visual quality assessment

The subjective methodology of paired comparison is currently recognized as the most precise methodology. However, it is not widely used because it implies a larger number of assessments than other methodologies and therefore a longer test duration. In this paper we investigate how sorting algorithms can be used to decrease the duration of such experiments by selecting only a fraction of all the possible comparisons. Three methods based on the insertion sort and the binary tree algorithms are applied to paired-comparison data. Performance analysis shows interesting trade-offs between the number of comparisons and the accuracy of the results.
Optimal context quantization in lossless compression of image data sequences

In image compression context-based entropy coding is commonly used. A critical issue to the performance of context-based image coding is how to resolve the conflict of a desire for large templates to model high-order statistic dependency of the pixels and the problem of context dilution due to insufficient sample statistics of a given input image. We consider the problem of finding the optimal quantizer Q that quantizes the K-dimensional causal context C/sub t/=(X/sub t-t1/,X/sub t-t2/,...,X/sub t-tK/) of a source symbol X/sub t/ into one of a set of conditioning states. The optimality of context quantization is defined to be the minimum static or minimum adaptive code length of given a data set. For a binary source alphabet an optimal context quantizer can be computed exactly by a fast dynamic programming algorithm. Faster approximation solutions are also proposed. In case of m-ary source alphabet a random variable can be decomposed into a sequence of binary decisions, each of which is coded using optimal context quantization designed for the corresponding binary random variable. This optimized coding scheme is applied to digital maps and /spl alpha/-plane sequences. The proposed optimal context quantization technique can also be used to establish a lower bound on the achievable code length, and hence is a useful tool to evaluate the performance of existing heuristic context quantizers.
Tree coding of bilevel images

Presently, sequential tree coders are the best general purpose bilevel image coders and the best coders of halftoned images. The current ISO standard, Joint Bilevel Image Experts Group (JBIG), is a good example. A sequential tree coder encodes the data by feeding estimates of conditional probabilities to an arithmetic coder. The conditional probabilities are estimated from co-occurrence statistics of past pixels, the statistics are stored in a tree. By organizing the code length calculations properly, a vast number of possible models (trees) reflecting different pixel orderings can be investigated within reasonable time prior to generating the code. A number of general-purpose coders are constructed according to this principle. Rissanen's one-pass algorithm, context, is presented in two modified versions. The baseline is proven to be a universal coder. The faster version, which is one order of magnitude slower than JBIG, obtains excellent and highly robust compression performance. A multipass free tree coding scheme produces superior compression results for all test images. A multipass free template coding scheme produces significantly better results than JBIG for difficult images such as halftones. By utilizing randomized subsampling in the template selection, the speed becomes acceptable for practical image coding.
Lossless Compression of Stereo Disparity Maps for 3D

Efficient compression of disparity data is important for accurate view synthesis purposes in multi-view communication systems based on the "texture plus depth" format, including the stereo case. In this paper a novel technique for loss less compression of stereo disparity images is presented. The coding algorithm is based on bit-plane coding, disparity prediction via disparity warping and context-based arithmetic coding exploiting predicted disparity data. Experimental results show that the proposed compression scheme achieves average compression factors of about 48:1 for high resolution disparity maps for stereo pairs and outperforms different standard solutions for loss less still image compression. Moreover, it provides a progressive representation of disparity data as well as a parallelizable structure.
Parallel iterative decoding of Transform Domain Wyner-Ziv video using cross bitplane correlation

In recent years, Transform Domain Wyner-Ziv (TDWZ) video coding has been proposed as an efficient Distributed Video Coding (DVC) solution, which fully or partly exploits the source statistics at the decoder to reduce the computational burden at the encoder. In this paper, a parallel iterative LDPC decoding scheme is proposed to improve the coding efficiency of TDWZ video codecs. The proposed parallel iterative LDPC decoding scheme is able to utilize cross bitplane correlation during decoding, by iteratively refining the soft-input, updating a modeled noise distribution and thereafter enhancing the bitplane decoding performance. Experimental results show that the proposed scheme reduces the bit rate of Wyner-Ziv frames up to 5.6% and improves the rate-distortion (RD) performance of TDWZ.
Entropy Bounds for Constrained Two-Dimensional Random Fields

The maximum entropy and thereby the capacity of two-dimensional (2-D) fields given by certain constraints on configurations is considered. Upper and lower bounds are derived. A new class of 2-D processes yielding good lower bounds is introduced. Asymptotically, the process achieves capacity for constraints with limited long-range effects. The processes are general and may also be applied to, e.g., data compression of digital images. Results are given for the binary hard square model, which is a 2-D run-length-limited model and some other 2-D models with simple constraints.
Image coding using Markov models with hidden states

Lossless image coding may be performed by applying arithmetic coding sequentially to probabilities conditioned on the past data. Therefore the model is very important. A new image model is applied to image coding. The model is based on a Markov process involving hidden states. An underlying Markov process called the slice process specifies D rows with the width of the image. Each new row of the image coincides with row N of an instance of the slice process. The N-1 previous rows are read from the causal part of the image and the last D-N rows are hidden. This gives a description of the current row conditioned on the N-1 previous rows. From the slice process we may decompose the description into a sequence of conditional probabilities, involving a combination of a forward and a backward pass. In effect the causal part of the last N rows of the image becomes the context. The forward pass obtained directly from the slice process starts from the left for each row with D-N hidden rows. The backward pass starting from the right additionally has the current row as hidden. The backward pass may be described as a completion of the forward pass. It plays the role of normalizing the possible completions of the forward pass for each pixel. The hidden states may effectively be represented in a trellis structure as in an HMM. For the slice process we use a state of D rows and V-1 columns, thus involving V columns in each transition. The new model was applied to a bi-level image (SO9 of the JBIG test set) in a two-part coding scheme.
Adaptive deblocking and deringing of H.264/AVC video sequences

We present a method to reduce blocking and ringing artifacts in H.264/AVC video sequences. For deblocking, the proposed method uses a quality measure of a block based coded image to find filtering modes. Based on filtering modes, the images are segmented to three classes and a specific deblocking filter is applied to each class. Deringing is obtained by an adaptive bilateral filter; spatial and intensity spread parameters are selected adaptively using texture and edge mapping. The analysis of objective and subjective experimental results shows that the proposed algorithm is effective in deblocking and deringing low bit-rate H.264 video sequences.
No-Reference Video Quality Assessment using MPEG analysis

We present a method for No-Reference (NR) Video Quality Assessment (VQA) for decoded video without access to the bitstream. This is achieved by extracting and pooling features from a NR image quality assessment method used frame by frame. We also present methods to identify the video coding and estimate the video coding parameters for MPEG-2 and H.264/AVC which can be used to improve the VQA. The analysis differs from most other video coding analysis methods since it is without access to the bitstream. The results show that our proposed method is competitive with other recent NR VQA methods for MPEG-2 and H.264/AVC.
The emerging JBIG2 standard

The Joint Bi-Level Image Experts Group (JBIG), an international study group affiliated with ISO/IEC and ITU-T, is in the process of drafting a new standard for lossy and lossless compression of bilevel images. The new standard, informally referred to as JBIG2, will support model-based coding for text and halftones to permit compression ratios up to three times those of existing standards for lossless compression. JBIG2 will also permit lossy preprocessing without specifying how it is to be done, In this case, compression ratios up to eight times those of existing standards may be obtained with imperceptible loss of quality. It is expected that JBIG2 will become an international standard by 2000.
Comparing subjective and objective quality assessment of HDR images compressed with JPEG-XT

In this paper a subjective test in which participants evaluate the quality of JPEG-XT compressed HDR images is presented. Results show that for the selected test images and display, the subjective quality reached its saturation point starting around 3bpp. Objective evaluations are obtained by applying a model of the display and providing the modeled images to three objective metrics dedicated to HDR content. Objective grades are compared with subjective data both in physical domain and using a gamma correction to approximate perceptually uniform luminance coding. The MRSE metric obtains the best performance with the limit that it does not capture the quality saturation. The usage of the gamma correction prior to applying metrics depends on the characteristics of each objective metric.
Error resilient H.264/AVC video over satellite for low packet loss rates

The performance of video over satellite is simulated. The error resilience tools of intra macroblock refresh and slicing are optimized for live broadcast video over satellite. The improved performance using feedback, using a cross- layer approach, over the satellite link is also simulated. The new Inmarsat BGAN system at 256 kbit/s is used as test case. This systems operates at low loss rates guaranteeing a packet loss rate of not more than 10~3. For high-end applications as 'reporter-in-the-field' live broadcast, it is crucial to obtain high quality without increasing delay.
Re-estimation of Motion and Reconstruction for Distributed Video Coding

Transform domain Wyner-Ziv (TDWZ) video coding is an efficient approach to distributed video coding (DVC), which provides low complexity encoding by exploiting the source statistics at the decoder side. The DVC coding efficiency depends mainly on side information and noise modeling. This paper proposes a motion re-estimation technique based on optical flow to improve side information and noise residual frames by taking partially decoded information into account. To improve noise modeling, a noise residual motion re-estimation technique is proposed. Residual motion compensation with motion updating is used to estimate a current residue based on previously decoded frames and correlation between estimated side information frames. In addition, a generalized reconstruction algorithm to optimize a multihypothesis reconstruction is proposed. The proposed techniques using motion and reconstruction re-estimation (MORE) are integrated in the SING TDWZ codec, which uses side information and noise learning. For Wyner-Ziv frames using GOP size 2, the MORE codec significantly improves the TDWZ coding efficiency with an average (Bjøntegaard) PSNR improvement of 2.5 dB and up to 6 dB improvement compared with DISCOVER.
Ultrahigh-Spectral-Efficiency WDM/SDM Transmission Using PDM-1024-QAM Probabilistic Shaping With Adaptive Rate

We demonstrate wavelength-division-multiplexed (WDM) and space-division-multiplexed (SDM) transmission of probabilistically shaped polarization-division-multiplexed (PDM) 1024-state quadrature amplitude modulation (QAM) channels over a 9.7-km single-mode 30-core fiber, achieving aggregated spectral efficiency of 297.82 bit/s/Hz on a 12.5-GHz grid and 7.01-Tbit/s spatial-super-channel on a 25-GHz grid without multiple-input multiple-output (MIMO) processing. Actual soft-decision forward error correction (SD-FEC) decoding was employed to obtain error-free performance, and adaptive rates and spectral efficiencies for individual WDM/SDM channels have been applied according to their channel conditions, by adjusting the SD-FEC overhead without changing the modulation format. Probabilistically shaped PDM-1024-QAM has been used to further increase the aggregated achievable rate due to the added performance improvement through shaping gain.
Multiple LDPC decoding using bitplane correlation for Transform Domain Wyner-Ziv video coding

Distributed video coding (DVC) is an emerging video coding paradigm for systems which fully or partly exploit the source statistics at the decoder to reduce the computational burden at the encoder. This paper considers a Low Density Parity Check (LDPC) based Transform Domain Wyner-Ziv (TDWZ) video codec. To improve the LDPC coding performance in the context of TDWZ, this paper proposes a Wyner-Ziv video codec using bitplane correlation through multiple parallel LDPC decoding. The proposed scheme utilizes inter bitplane correlation to enhance the bitplane decoding performance. Experimental results show that the proposed scheme reduces the bit rate up to 3.9% and improves the rate-distortion (RD) performance of TDWZ.
Low delay Wyner-Ziv coding using optical flow

Distributed Video Coding (DVC) is a video coding paradigm that exploits the source statistics at the decoder based on the availability of the Side Information (SI). The SI can be seen as a noisy version of the source, and the lower the noise the higher the RD performance of the decoder. The SI is usually generated by means of interpolation-based methods, which rely on the availability of a preceding and a following frame with respect to the to-be-decoded one. These methods lead to relatively high RD performance but also high delays. This work is focused on a low-delay DVC codec, relying only on preceding frames for the generation of the SI by means of Optical Flow (OF), which is also used in the refinement step of the SI for enhanced RD performance. Compared with a state-of-the-art extrapolation-based decoder the proposed solution achieves RD BjØntegaard gains up to 1.3 dB.