nils-herrmann/clean_scirepeval huggingface dataset:
Correlation-based watermarking for halftone images

In many printer and publishing applications, it is desirable to embed data in halftone images for copyright control and authentication purposes. While intentional attacks on printed matters may not be likely, unintentional attacks such as cropping and distortion due to dirt or human writing/marking are likely. In this paper, we propose a novel halftone image watermarking method called Correlation-based Watermarking Error Diffusion (CWED) to embed a full-size, high-resolution and robust binary image in a single halftone image during halftoning while introducing minimal distortion. Since CWED uses the stochastic properties of halftone images to embed the watermark throughout the whole image, CWED is robust to cropping and distortion due to dirt or marking.
Fast sub-pixel motion estimation with simplified modeling in HEVC

Motion estimation (ME) is one of the key elements in video coding standard which eliminates the temporal redundancies between successive frames. In recent international video coding standards, sub-pixel ME is proposed for its excellent coding performance. Compared with integer-pixel ME, sub-pixel ME needs interpolation to get the value in sub-pixel position. Also, Hadamard transform will be applied in order to achieve better performance. Therefore, it is becoming more and more critical to develop fast sub-pixel ME algorithms. In this paper, a novel fast sub-pixel ME algorithm is proposed which makes full use of 8 neighboring integer-pixel points. This algorithm models the error surface in sub-pixel position by a second order function with five parameters two times to predict the best sub-pixel position. Experimental results show that the proposed method can reduce the complexity significantly with negligible quality degradation.
Data hiding in dot diffused halftone images

In this paper, we propose two halftone image watermarking methods. Data Hiding by Conjugate Dot Diffusion (DHCDD) and Data Hiding by Dual Conjugate Dot Diffusion (DHD-CDD). DHDCDD is an improved method of DHCDD. Both of these two methods can embed a secret pattern into two halftone images. When the two halftone images are overlaid, the secret pattern will be revealed. Compared to the recent method Noise Balanced Dot Diffusion, the experimental results show that the proposed methods are better in both Correct Decoding Rate and the visual quality of the revealed hidden pattern.
Rate-distortion optimized block classification and bit allocation in screen video compression

Due to the divergent characteristics of image contents and text contents in screen videos, how to make the joint optimization leveraging rate-distortion (R-D) optimized block classification and bit allocation is critical to the compression performance. In this paper, a general model-based solution is proposed as an attempt to solve this problem. The contributions of this paper are twofold: First, the rate and distortion characteristics of image blocks and text blocks in block-based content-adaptive screen video encoder (BASC) are carefully studied, and the rate and distortion models are proposed. Second, with the proposed rate and distortion models, the R-D optimized block classification and bit allocation are derived using bisection searched Lagrange multiplier method. Experimental results demonstrate that the proposed R-D optimized block classification and bit allocation algorithms are able to adapt to diverse screen contents, which results in a significant gain of up to 4.5dB in PSNR.
A set of mutually watermarked halftone images

In this paper, we propose a method called, modified stochastic error diffusion (MSED), to hide an invisible binary visual pattern in a set of error diffused halftone images. MSED can embed a dithered watermark in multiple halftone images while preserving the visual quality of each halftone image. When any of these images overlapping with any others, the watermark will appear. In case that many of these image overlapped together, the contrast of watermark becomes clearer.
A novel motion estimation algorithm for arbitrarily shaped video coding

In this paper, we present a fast motion estimation algorithm for arbitrarily shaped video coding in MPEG-4. This novel algorithm takes advantage of our new discovery about the close relation between the best matching block and its shape information-alpha plane. Without any additional padding computation, this new motion estimation algorithm computes the sum of absolute difference (SAD) between two alpha planes rather than the pixels' intensities. Compared with the full search block-matching algorithm (recommended in the MPEG-4 standard), the proposed algorithm achieves an impressive speed-up ratio with very minor quality degradation and little bit-count increase. Extensive simulations are provided in this paper to demonstrate this fact.
Video Coding on Multicore Graphics Processors

In this article, we investigate using multi-core graphics processing units (GPUs) for video encoding and decoding. After an overview of video coding and GPUs, we review some previous work on structuring video coding modules so that the massive parallel processing capability of GPUs can be harnessed. We also review previous work on partitioning the video decoding flow between the central processing unit (CPU) and GPU. After that, we discuss in detail a GPU based fast motion estimation to illustrate some design considerations in using GPUs for video coding, and the tradeoff between speedup and rate-distortion performance. Our results highlight the importance to expose as much data parallelism as possible in designing algorithms for GPUs.
Enhanced predictive motion vector field adaptive search technique (E-PMVFAST)-based on future MV prediction

Motion estimation (ME) is a core part of most modern video coding standard, and it directly affects the compression efficiency and visual quality of a video. If full search (FS) algorithm is used, ME could takes over 70% of computational power. Many algorithms such as TSS and PMVFAST, have been developed to achieve great speed up for ME. In this paper, a new algorithm enhanced-PMVFAST (E-PMVFAST) is proposed, which performs better than most if not all other existing algorithms in terms of speed up factor while keeping the similar PSNR with FS. Our experiments have also verified the robustness of the proposed E-PMVFAST algorithm.
Stereo matching by adaptive weighting selection based cost aggregation

Cost aggregation is the most essential step for dense stereo correspondence searching, which measures the similarity between pixels in the stereo images. In this paper, based on the analysis of the optimal adaptive weight, we propose a novel support aggregation strategy by adaptive weighting selection. The proposed method calculates the aggregation cost by the joint optimization of both left and right matching cost. By assigning more reasonable weighting coefficients, we exclude the occlusion pixels while preserving sufficient support region for accurate matching. The proposed optimal strategy can be integrated by any other adaptive weighting based cost aggregation method to generate more reasonable similarity measurement. Experimental results show that, compare with traditional methods, our algorithm can reduce the foreground fatten phenomenon while increasing the accuracy in the high texture regions.
Motion Estimation for H.264/AVC using Programmable Graphics Hardware

We present an efficient implementation of motion estimation (ME) for H.264/AVC using programmable graphics hardware. The cost function for ME in H.264/AVC depends on the motion vector (MV) predictor which is the median MV of three neighboring coded blocks. Previous implementations assume no dependency among adjacent blocks, which is not true for H.264/AVC, they also perform unsatisfactorily because of their low arithmetic intensity, which is defined as operation per word transferred. To overcome the dependency problem, we introduce a new implementation which performs ME on block-by-block basis. Moreover, we can adjust the arithmetic intensity easily to optimize the performance on different graphics cards. Experimental results show that our implementation is substantially faster (by 10 times) than our SIMD optimized CPU implementation
2-SiMDoM: A 2-Sieve model for detection of mitosis in multispectral breast cancer imagery

In this paper, we propose a 2-Sieve model for the detection of mitosis in breast cancer multispectral images. Multiresolution wavelet features & Gray Level Entropy Matrix (GLEM) features have been computed for each candidate on all the spectral bands. A novel dimensionality selection algorithm has been introduced and its performance compared with other existing algorithms. Data imbalance and data cleaning have been taken care of using classical data mining techniques. Furthermore, a Second Sieve classification is performed to increase the Positive Predictive Value (PPV) with minimal loss in Sensitivity. A final Sensitivity and PPV of 82.35% & 73.04% respectively was achieved over the testing set using the proposed scheme.
Three-loop temporal interpolation for error concealment of MDC

Multiple description coding (MDC) can be used as an error resilience (ER) technique for video coding. In case of transmission errors, error concealment can be combined with MDC to reconstruct the lost frame, such that the propagated error to the following frames is reduced. In this paper, we propose a new temporal error concealment method named three-loop temporal interpolation (TLTI). TLTI can be well combined with temporal sub-sampling ER methods, such as MDC and alternative motion-compensated prediction. In the simulation, we compare the performance of TLTI with unidirectional motion compensated temporal interpolation (UMCTI). Both visual and quantitive results show that TLTI can achieve a better video quality than UMCTI
N-dimensional zonal algorithms. The future of block based motion estimation?

The popularity of zonal based algorithms for block based motion estimation has been increasing due to their superior performance in both terms of reduced complexity and superior quality versus other preexisting algorithms. In our previous work we mainly focused on generalizing the different parameters used in these algorithms and finding the possibly most efficient implementation. In this paper a further generalization of these algorithms is presented, where instead we mainly consider the way zones can be designed and what should be the ultimate goal of such an algorithm. As a result we present a framework of algorithms which can have applications not only in video coding, but also in other video signal processing areas, such as computer vision, video analysis, salient stills etc. We do so by initially considering the dimensionality of video data and how it can be most efficiently analyzed and exploited in the context of zonal algorithms. A formulization of these algorithms is then presented, according to which different implementations for different applications can be selected. Simulation results, for the simple 3-D case using the predictive diamond search (PDS) algorithm, a low complexity zonal algorithm, demonstrate the efficacy of the proposed techniques while still having low complexity. Higher order implementations using more dimensions and more efficient zonal algorithms can also be considered.
A tutorial on image/video coding standards

The field of image and video compression has gone through rapid growth during the past thirty years, leading to various coding standards. The main goal of continuous efforts on image/video coding standardization is to achieve low bit rate for data storage and transmission, while maintaining acceptable distortion. In this paper, various developmental stages of image and video compression standards are reviewed, including JPEG and JPEG 2000 image standards, MPEG-1, MPEG-2, MPEG-4, H.261, H.263, H.264/MPEG-4 AVC, and the latest international video standard HEVC as well as Chinese video coding standard AVS. Key features and major applications of the standards will be briefly introduced and the compression performance of the standards at each stage will be compared and discussed.
Laplacian Mixture Model(LMM) based frame-layer rate control method for H.264/AVC high-definition video coding

Accurate statistical distribution for estimating the transformed residues is greatly important for us to analyze the rate-distortion behavior of video encoders. However, the previous work pays more attention to those sequences of low-resolution. In this paper, we address the statistical characteristics of DCT coefficients of high-definition videos coded by H.264/AVC. The contribution of this paper is threefold: First, Laplacian Mixture Model (LMM) is proposed to model the residues instead of using Laplacian or Cauchy distributions; the corresponding new rate-distortion model based on LMM is presented next; based on this new rate-distortion model, one frame-layer rate control algorithm is developed. Experimental results showed that the proposed rate control method achieves an improvement of PSNR up to 0.52dB with less visual quality variation compared to JM 11.0.
A novel approach to fast multi-block motion estimation for H.264 video coding

The upcoming video coding standard, H.264, uses motion estimation with multiple block sizes to improve the rate-distortion performance. However, full exhaustive search of all block sizes is computational intensive with complexity increasing linearly with the number of allowed block size. In this paper, a novel fast multi-block motion estimation (FMBME) is proposed for H.264 video coding. Experimental results show that the proposed FMBME can efficiently reduce the computational cost by 40.73% with similar visual quality and bit rate.
Efficient rate control technique for JPEG2000 image coding using priority scanning

Most of the computation and memory usage of the post-compression rate-distortion (PCRD) optimization scheme in JPEG2000 are redundant. In this paper, we propose a novel rate control scheme using priority scanning (PS) to address the problem. By encoding the truncation points in a different order based on the priority information, the proposed method can totally remove the redundancy while keeping a similar quality to the PCRD scheme. The proposed scheme can efficiently reduce both the computational cost and working memory size of the entropy coding process by up to about 90%, in the case of 0.25 bpp (1/32) compression.
Data hiding in halftone images by conjugate error diffusion

In this paper, we propose a method called Data Hiding by Conjugate Error Diffusion (DHCED) to hide invisible patterns in two or more error diffused halftone images such that the hidden patterns would appear on the halftone images when they are overlaid. A binary visual pattern is hidden in two halftone images by applying Conjugate Error Diffusion. Simulation results show that the two halftone images have good visual quality, and the hidden pattern is visible when the two halftone images are overlaid. Compared with the old proposed method, Data Hiding by Stochastic Error Diffusion (DHSED), the contrast between the hidden pattern and the background is greatly improved. The minor trace of edge generated by the phase change of DHSED is also eliminated.
Frame Complexity Guided Lagrange Multiplier Selection for H.264 Intra-Frame Coding

Rate-distortion (R-D) optimized mode decision plays an essential role in H.264/AVC encoding. Among all the possible coding modes, it aims to select the one which has the best trade-off between bitrate and compression distortion. Specifically, this tradeoff is tuned through the choice of the Lagrange multiplier. However, in the H.264 reference software, the value of Lagrange multiplier is only determined by the quantization stepsize, with no consideration of the characteristics of the input signal. In this paper, we address the problem of optimal Lagrange determination for intra-frames in a signal dependent environment, and a novel frame complexity guided Lagrange multiplier selection method is introduced. Our contributions are two-fold. First, we propose a novel signal dependent R-D model which uses the average input frame gradient to measure the signal complexity. Second, based on the proposed signal dependent R-D model, we derive the closed-form solution of the optimal Lagrange multiplier. Experimental results suggest that up to 8.18% bitrate reduction can be obtained with negligible additional computation complexity.
Perceptual compressive sensing for image signals

Human eyes have different sensitivity to different frequency components of image signals, typically, low frequency components are relatively more crucial to the perceptual quality of images than high frequency components. Based on this observation, we propose a novel sampling scheme for compressive sensing framework by designing a weighting scheme for the sampling matrix. By adjusting the weighting coefficients, we can tune the structure of the sampling matrix to favor the frequency components that are important to human perception, so that those components could be more precisely recovered in the reconstruction procedure. Experimental results reveal that our proposed scheme can greatly enhance the performance of compressive sensing framework in both PSNR and visual quality without increasing the complexity of the framework structure or computational procedure.
Adaptive joint demosaicing and Subpixel-based Down-sampling for Bayer image

A digital camera provided with a Bayer pattern single sensor needs color interpolation to reconstruct a full color image. To show high resolution image on a lower resolution display, it must then be down-sampled. These two steps influence each other, i.e., the color artifacts introduced in demosaicing may be magnified in subsequent down-sampling process and vice versa. Thanks to the fact that LCD displays are actually composed of separable subpixels, which can be individually addressed to achieve a higher effective apparent resolution. This paper presents an Adaptive Joint Demosaicing and Subpixel-based Down-sampling scheme (AJDSD) for single-sensor camera image, where the subpixel-based down-sampling is adaptively and directly applied in Bayer domain, without the process of demosaicing. Simulation results demonstrate that when compared with conventional “demosaicing-first and down-sampling-later” methods, AJDSD achieves superior performance improvement in terms of computational complexity. As for visual quality, AJDSD is more effective in preserving high frequency details, leading to much sharper and clearer results.
Novel blind multiple watermarking technique for images

Three novel blind watermarking techniques are proposed to embed watermarks into digital images for different purposes. The watermarks are designed to be decoded or detected without the original images. The first one, called single watermark embedding (SWE), is used to embed a watermark bit sequence into digital images using two secret keys. The second technique, called multiple watermark embedding (MWE), extends SWE to embed multiple watermarks simultaneously in the same watermark space while minimizing the watermark (distortion) energy. The third technique, called iterative watermark embedding (IWE), embeds watermarks into JPEG-compressed images. The iterative approach of IWE can prevent the potential removal of a watermark in the JPEG recompression process. Experimental results show that embedded watermarks using the proposed techniques can give good image quality and are robust in varying degree to JPEG compression, low-pass filtering, noise contamination, and print-and-scan.
Distributed Soft Video Broadcast (DCAST) with Explicit Motion

Video broadcasting is a popular application of wireless network. However, the existing layered approaches can hardly accommodate users with diverse channel conditions as analog communication can do. The newly emerged `soft cast' approach, utilizing soft broadcast, provides smooth multicast performance but is not very efficient in inter frame compression. In this work, we propose a motion-aligned wireless video multicast scheme DCAST. Instead of using conventional close loop prediction (CLP), DCAST is based on distributed source coding (DSC) theory. This helps DCAST to avoid error propagation but still achieve high compression efficiency in inter frame coding. DCAST outperforms soft cast 5dB in video PSNR while maintaining the similar graceful degradation feature as soft cast.
High bit-precision image acquisition and reconstruction by planned sensor distortion

We present a novel framework for high bit-precision image acquisition and reconstruction. This framework is designed based on the inherent Markov property of image signals. In acquisition stage, we add planned sensor distortion (PSD) to the analog image signal before feeding it to A/D converters (or quantizers) in camera sensor. In reconstruction stage, the acquired quantized pixel values are jointly combined to get the reconstructed signal with reduced uncertainty range. Advantages of proposed PSD framework include 1) simplicity: it does not require any change to the core hardware of existing A/D converters; 2) effectiveness: experiment results demonstrate significant PSNR gain over traditional methods (up to 10 dB when quantizer bit-depth is relatively low); and 3) generality: this framework can also be applied for acquisition of other analog signals, including audio, video, etc.
Joint Demosaicing and Subpixel-Based Down-Sampling for Bayer Images: A Fast Frequency-Domain Analysis Approach

A portable device such as a digital camera with a single sensor and Bayer color filter array (CFA) requires demosaicing to reconstruct a full color image. To display a high resolution image on a low resolution LCD screen of the portable device, it must be down-sampled. The two steps, demosaicing and down-sampling, influence each other. On one hand, the color artifacts introduced in demosaicing may be magnified when followed by down-sampling; on the other hand, the detail removed in the down-sampling cannot be recovered in the demosaicing. Therefore, it is very important to consider simultaneous demosaicing and down-sampling.
Temporal search range prediction based on a linear model of motion-compensated residue

An efficient temporal search range prediction method is proposed to reduce the complexity of multiple reference frames motion estimation (MRFME) in video coding. Based on a linear model of motion-compensated residue, the behavior of residues under MRFME is investigated, and the gain of multiple reference frames is analyzed. The proposed method utilizes the current residue to estimate the gain of searching more reference frames, and predicts the temporal search range that maintains the coding performance with minimum complexity. Experimental results show that the proposed scheme can significantly reduce the complexity in motion estimation while the degradation of the coding performance is negligible.
An efficient optimal rate control scheme for JPEG2000 image coding

Most of the computation and memory usage of the post-compression rate-distortion (PCRD) optimization scheme in JPEG2000 are redundant. In this paper, an efficient rate PCRD scheme based on priority scanning (PS) is proposed to alleviate the problem. By encoding the truncation points in a different order based on the priority information, the proposed method can efficiently reduce the redundancy while keeping the same quality as the conventional PCRD scheme. The proposed scheme can efficiently reduce both the computational cost and working memory size of the entropy coding process by up to 52% and 71%, in the case of 0.25 bpp (1/32) compression, respectively.