nils-herrmann/clean_scirepeval huggingface dataset:
A multi-bit robust watermark 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 proposed a novel halftone image watermarking method to embed a robust, invisible, multi-bit watermark in the halftone images during halftoning while introducing minimal distortion.
Improved combined inter-intra prediction using spatial-variant weighted coefficient

In current video coding standard H.264/AVC, pixel prediction is applied to reduce spatial and temporal redundancy existed in video signal. Previously, it has been shown that better coding performance is achieved compared to H.264/AVC by combining inter and intra prediction to generate a more accurate prediction. In this paper, an improved combined prediction scheme is presented which allows the video codec to tune weighted coefficient for inter prediction and intra prediction adaptively to local signal characteristics. In order to avoid additional overhead signalling, statistics of already coded neighboring block is analyzed to predict the weighted coefficients of the combined prediction for current macroblock. Compared to H.264/AVC, coding performance is increased by up to 1.9%. And compared to the latest combined prediction method using spatial-invariant weighted coefficients, simulation results show that the proposed scheme achieves additional coding gain of up to 0.66%.
A fast variable block size motion estimation algorithm with refined search range for a two-layer data reuse scheme

Motion estimation (ME) serves as a key tool in a variety of video coding standards. With the increasing need for higher resolution video format, the limited memory bandwidth becomes a bottleneck for ME implementation. The huge data loading from external memory to the on-chip memory and the frequent data fetching from the on-chip memory to the ME engine are two major problems. To reduce both off-chip and on-chip memory bandwidth, we propose a two-layer data reuse scheme. On the macroblock (MB) layer, an advanced Level C data reuse scheme is presented. It employs two cooperating on-chip caches which load data in a novel local-snake scanning manner. On the block layer, we propose a fast variable block size motion estimation with a refined search window (RSW-VBSME). A new approach for hardware implementation of VBSME is then employed based on the fast algorithm. Instead of obtain the SADs of all the modes at the same time, the ME of different block sizes are performed separately. This enables higher data reusability within an MB. The two-layer data reuse scheme archives a more than 90% reduction of off-chip memory bandwidth with a slight increase of on-chip memory size. Moreover, the on-chip memory bandwidth is also greatly reduced compared with other reuse methods with different VBSME implementations.
Content adaptive watermarking using a 2-stage predictor

Digital watermarking is one of the solutions to protect intellectual properties and copyright by hiding information, such as a random sequence or a logo, into digital media. In this paper, a new watermarking scheme is proposed. The embedding process takes place in the spatial domain. A bi-level logo is embedded into digital media by comparing the absolute difference between the original pixel value and the predicted pixel value, which is computed and predicted by using the concept of activity measurement, followed by spatial varying filter (SVF). The logo will be extracted from a possibly corrupted image, without the help of original uncorrupted image. The new proposed algorithm can withstand the geometric attacks as well as common signal processing, and has a high payload capacity.
Reducing the ciphertext expansion in image homomorphic encryption via linear interpolation technique

Homomorphic encryption becomes one of the key components in many emerging applications, e.g., cloud computing, to achieve privacy-preserving data processing. However, one of the major drawbacks that precludes the widespread adoption of homomorphic encryption is the huge expansion of the ciphertext. This problem becomes even more severe when multimedia data (images/videos) are handled, as these files are essentially of large sizes. This work addresses this challenging issue and proposes a strategy of reducing the ciphertext expansion in image homomorphic encryption. To this end, a randomly selected subset of the pixels are encrypted using homomorphic cryptosystem to form one part of the ciphertext. The remaining pixels are encrypted by relating them with this random subset through a linear interpolation technique. The whole homomorphically encrypted image can be obtained, upon receiving all the ciphertexts, by exploiting the homomorphic property and the linearity. It is demonstrated that the proposed scheme is secure, and is capable of achieving significant reduction of the ciphertext expansion, while perfectly preserving the homomorphic property.
Reweighted Compressive Sampling for image compression

Compressive Sampling (CS), is an emerging theory which points us a promising direction of designing novel efficient data compression techniques. However, the conventional CS adopts a non-discriminated sampling scheme which usually gives poor performance on realistic complex signals. In this paper we propose a reweighted Compressive Sampling for image compression. It introduces a weighting scheme into the conventional CS framework whose coefficients are determined in encoding side according to the statistics of image signals. Experimental results demonstrate that our proposed method notably outperforms the conventional Compressive Sampling framework in coding performance in the sense that the reconstruction quality is greatly enhanced with same number of measurements and computational complexity.
Personal photo album compression and management

The advance in multimedia technologies have resulted in an explosive growth of pictures in personal computers and in cloud. Typically many pictures taken in the same occasion are similar. The cost to store and transmit them can be very significant. Thus it is important to find an efficient method to store these pictures. This paper proposed a compression scheme for similar images. Our approach is to arrange all the similar images into tree structure then apply video coding technique along each branch. To maximize the inter-image correlation between adjacent photos, we consider the minimum spanning tree (MST) subjecting to a maximum depth limit to ensure fast access to all images. This structure is encoded by the latest video coding technique High Efficiency Video Coding (HEVC), which is reported to has advantage in high definition video/image compression. It also supports deleting, adding and modifying images. Experiments show that the proposed method saved 75% space comparing to JPEG format.
Improved halftone image data hiding with intensity selection

In this paper, we propose a novel algorithm called intensity selection (IS) that can be applied to three existing halftone image data hiding algorithms DHST, DHPT and DHSPT to achieve improved visual quality. The proposed IS algorithm generalizes the hidden data representation and select the best location out of a set of candidate locations for the application of DHST/DHPT/DHSPT. It chooses pixel locations that are either very bright or very dark. The IS requires the inverse-halftoned image which implies potentially high computation requirement. Experiments suggest that significant improvement in visual quality can be achieved.
Novel temporal domain hole filling based on background modeling for view synthesis

View synthesis is a technique to generate images/videos in a virtual viewpoint. In this paper, the dis-occlusion/hole problem in view synthesis is resolved from the temporal domain. By the fact that dis-occlusions belong to the background, firstly we build an online background under a newly designed Switchable Gaussian Model (SGM), owning to its computationally simplicity and scene adaptivity. Then, real textures in the dis-occlusions are able to be recovered with the built background. Experimental results have verified the improvements in rendering quality and computation complexity by comparing to the conventional spatial filling methods and other temporal filling methods.
Efficient Scene Change Detection in MPEG Compressed Video Using Composite Block Averaged Luminance Image Sequence

In video indexing and segmentation, scene change detection is an essential step. However, the problems of inaccuracy and high complexity for existing scene change detection methods are still unsolved. This work presents several advance methods using the partially decoded information from MPEG compressed video. The DC composite image sequence is proposed for scene change detection. DC sequence is widely used for scene change detection in compressed domain. We further improve the efficiency of extraction by eliminating the DC interpolation step. Several advance techniques are used to improve the accuracy and precision of the coarse approximated DC composite image. Experimental results show that our new proposed method can successfully detect both abrupt and gradual scene transition with lower complexity and better performance than the traditional DC sequence methods
A novel content-adaptive interpolation

We propose a content-adaptive interpolation algorithm for digital images. The proposed algorithm makes use of a non-linear spatial varying filter (SVF), which is simple but efficient in enlarging images. First, edge orientation statistics in an image are exploited, and then the information is used to determine suitable weights of neighboring pixels when interpolating the current pixel. Using the proposed approach, we can generate pixel values to replace missing ones such that sharp edges can be accurately reconstructed. Simulation results show that the proposed algorithm obtains better results in both subjective and objective quality measurements over existing methods.
An efficient two phase image interpolation algorithm based upon error feedback mechanism

Many image interpolation algorithms have been developed in the recent past aiming for high prediction accuracy. But these algorithms are focused only towards better predictor design. In this paper, we propose a generic two phase image interpolation algorithm based upon error feedback mechanism. In the first phase, we learn error pattern occurred during interpolation of down sampled version of original Low Resolution (LR) image. It is assumed that similar error pattern also occurrs during the interpolation of original LR image. Hence, error pattern learnt in first phase, is employed during the interpolation of original LR image (second phase). From extensive experiments, we found that our algorithm gives a significant improvement in prediction accuracy of existing interpolation algorithms. In particular, our algorithm plays a significant role in improving prediction accuracy of those algorithms which have inherently poor prediction capability for certain types of images.
Color clustering matting

Natural image matting refers to the problem of extracting regions of interest such as foreground object from an image based on user inputs like scribbles or trimap. More specifically, we need to estimate the color information of background, foreground and the corresponding opacity, which is an ill-posed problem inherently. Inspired by closed-form matting and KNN matting, in this paper, we extend the local color line model which is based on the assumption of linear color clustering within a small local window, to nonlocal feature space neighborhood. New affinity matrix is defined to achieve better clustering. Further, we demonstrate that good clustering ensures better prediction of alpha matte. Experimental evaluations on benchmark datasets and comparisons show that our matting algorithm is of higher accuracy and better visual quality than some state-of-the-art matting algorithms.
A spatial-temporal de-interlacing algorithm

In this paper, we proposed a spatial-temporal de-interlacing algorithm for conversion of interlaced video to progressive video. Our proposed algorithm estimates the motion trajectory of three consecutive fields interpolates the missing field along the motion trajectory. In the motion estimator, the unidirectional motion estimation and the bidirectional motion estimation processes are combined by multiple objective minimization technique. The unidirectional motion estimation estimates the motion trajectory by comparing the blocks from opposite parity fields while the bi-directional motion estimation compares blocks from the same parity fields. By combining the two motion estimations, the motion trajectory can be accurately predicted. In addition, a quality analyzer is proposed to evaluate the visual quality of the reconstructed frame, which chooses the appropriate interpolation scheme in order to provide maximum de-interlacing performance. Simulation results show the proposed algorithm has better performance over existing de-interlacing algorithm.
Secure Lempel-Ziv-Welch (LZW) algorithm with random dictionary insertion and permutation

In this paper, we propose an efficient encryption scheme by introducing randomness into the Lempel-Ziv-Welch (LZW) algorithm. This scheme utilizes random dictionary insertion and permutation, and incorporates with a bit-wise XOR module. Security analysis results show that the proposed scheme provides high level of security without any coding efficiency loss, compared with a standard LZW algorithm.
A novel ray-space based color correction algorithm for multi-view video

In multi-view video, color inconsistency among different views always exists because of imperfect camera calibration, CCD noise, etc. Since color inconsistency greatly reduces the coding efficiency and rendering quality of multi-view video, a novel ray-space based color correction algorithm is proposed in this paper. Firstly, for each epipolar plane image (EPI) in ray-space domain, feature points are extracted to form a corresponding feature EPI (FEPI). Secondly, radon transform is applied to each FEPI to detect corresponding points from different views and the average color is calculated from the detected corresponding points. Finally, for each viewpoint image, the optimal color correction matrix is calculated by minimizing the error energy between the color of the current view and the average color based on the least square error criteria. Experimental results show that the proposed algorithm greatly improves the color consistency among different views. Moreover, the coding efficiency of the corrected multi-view images is greatly improved compared to that of the original ones and the ones corrected by histogram matching method .
Simultaneous MAP-Based Video Denoising and Rate-Distortion Optimized Video Encoding

In this paper, a simultaneous MAP-based video denoising and rate-distortion optimized video encoding algorithm is proposed. We begin with formulating the denoising problem as a maximum a posteriori (MAP) estimate problem. Then, according to the Bayes rule, we show that the MAP estimate is determined by two terms: noise conditional density model and priori conditional density model. Based on the assumptions that the noise satisfies Gaussian distribution and the priori model is measured by the bit-rate, the MAP estimate can be expressed as a rate distortion optimization problem. With this, we are able to simultaneously perform MAP-based video denoising and rate-distortion optimized video encoding under some assumptions. Moreover, we describe in details how to select suitable coding parameters, i.e., quantization parameter, mode, motion vector, reference index, and regularization parameter. Finally, we conduct several experiments to verify our proposed algorithm.
Temporal Video Denoising Based on Multihypothesis Motion Compensation

Denoising module is required by any practical video processing systems. Most existing denoising schemes are spatio-temporal filters which operate on data over three dimensions. However, to limit the number of inputs, these filters only utilize one reference frame and cannot fully exploit temporal correlation. In this paper, a recursive temporal denoising filter named multihypothesis motion compensated filter (MHMCF) is proposed. To fully exploit temporal correlation, MHMCF performs motion estimation in a number of reference frames to construct multiple hypotheses (temporal predictions) of the current pixel. These hypotheses are combined by weighted averaging to suppress noise and estimate the actual current pixel value. Based on the multihypothesis motion compensated residue model presented in this paper, we investigate the efficiency of MHMCF, and some numerical evaluations are revealed. Experimental results show that MHMCF demonstrates quite good denoising performance while the inputs are much fewer than spatio-temporal filters. Moreover, as a purely temporal filter, it can well preserve spatial details and achieve satisfactory visual quality.
An adaptive center (radar) zonal based algorithm for motion estimation

In most video standards like MPEG 1/2/4 and ITU-T H.261/263, efficient motion estimation plays a key role in achieving high compression. A new algorithm is presented in this paper, which outperforms most of the traditional fast motion estimation algorithms in both speed and quality. It is also shown that in several cases, especially in video conferencing sequences the algorithm can achieve better visual quality, than the "optimal" but computationally intensive "full search" algorithm.
Novel 2-D MMSE Subpixel-Based Image Down-Sampling

Subpixel-based down-sampling is a method that can potentially improve apparent resolution of a down-scaled image on LCD by controlling individual subpixels rather than pixels. However, the increased luminance resolution comes at price of chrominance distortion. A major challenge is to suppress color fringing artifacts while maintaining sharpness. We propose a new subpixel-based down-sampling pattern called diagonal direct subpixel-based down-sampling (DDSD) for which we design a 2-D image reconstruction model. Then, we formulate subpixel-based down-sampling as a MMSE problem and derive the optimal solution called minimum mean square error for subpixel-based down-sampling (MMSE-SD). Unfortunately, straightforward implementation of MMSE-SD is computational intensive. We thus prove that the solution is equivalent to a 2-D linear filter followed by DDSD, which is much simpler. We further reduce computational complexity using a small k × k filter to approximate the much larger MMSE-SD filter. To compare the performances of pixel and subpixel-based down-sampling methods, we propose two novel objective measures: normalized l1 high frequency energy for apparent luminance sharpness and PSNRU(V) for chrominance distortion. Simulation results show that both MMSE-SD and MMSE-SD(k) can give sharper images compared with conventional down-sampling methods, with little color fringing artifacts.
A low latency cloud gaming system using edge preserved image homography

The emerging cloud gaming technology has been growing fast, driving up huge mobile consumer demands. The video streaming based cloud gaming scenario renders the game scenes in the cloud servers, and streams the encoded sequences to the thin clints where the game scenes are decoded and displayed to the players. However, current existing clouding gaming services have some problems, such as the latency and bandwidth limitation. The size of the video stream is usually quite large which requires heavy transmission. Worse still, the frame data rate will burst when the game scenes contain fast translation or rotation, resulting in strong latency problem. In this paper, we propose a novel video streaming based cloud gaming algorithm which reduces the burst of the frame rate significantly. There are mainly two innovations in this paper. Firstly, based on the analysis of the motion estimation strategy in the video codec, we introduce image homography technique for better motion prediction. Meanwhile, according to the rasterization rules of the game engine, we present a special designed interpolation algorithm named Edge Preserved Interpolation (EPI), for more accurate edge interpolation and further reduce the residues in the edge regions. The proposed algorithm is implemented on the x264 platform. Experimental results show that our algorithm has 18.0% BD-rate reduction compared with x264.
Chroma Intra Prediction Based on Inter-Channel Correlation for HEVC

In this paper, we investigate a new inter-channel coding mode called LM mode proposed for the next generation video coding standard called high efficiency video coding. This mode exploits inter-channel correlation using reconstructed luma to predict chroma linearly with parameters derived from neighboring reconstructed luma and chroma pixels at both encoder and decoder to avoid overhead signaling. In this paper, we analyze the LM mode and prove that the LM parameters for predicting original chroma and reconstructed chroma are statistically the same. We also analyze the error sensitivity of the LM parameters. We identify some LM mode problematic situations and propose three novel LM-like modes called LMA, LML, and LMO to address the situations. To limit the increase in complexity due to the LM-like modes, we propose some fast algorithms with the help of some new cost functions. We further identify some potentially-problematic conditions in the parameter estimation (including regression dilution problem) and introduce a novel model correction technique to detect and correct those conditions. Simulation results suggest that considerable BD-rate reduction can be achieved by the proposed LM-like modes and model correction technique. In addition, the performance gain of the two techniques appears to be essentially additive when combined.
A novel color interpolation framework in modified YCbCr domain for digital cameras

Most digital cameras use single-CCD with color filter array to capture sub-sampled digital color images and need color interpolation to generate full resolution color details. While most color interpolation algorithms are performed in the RGB domain, we propose a new framework to perform the color interpolation in the YCbCr domain. Simulation results suggest that the proposed framework can produce significantly better quality color images compared with existing algorithms.
Generalized lossless data hiding by multiple predictors

Digital watermarking is to prove the ownership and the authenticity of the media. However, as some applications, such as medical and military, are sensitive to distortion, this highlights the needs of lossless watermarking. In this paper, we propose a new lossless data hiding algorithm by using multiple predictors, which extends and generalizes our previous watermarking idea (Yip, 2005). By using different predictors with different characteristics, we can choose the embedding location to be low variance region or high variance region. The PSNR and the payload capacity are high and there are no "salt-and-peppers" artifacts
Comparison between block-based and pixel-based temporal interpolation for video coding

In very low bit rate (VLBR) video conferencing and videotelephony applications, the bit rate of sending video sequences must be kept low due to the limited channel bandwidth. To satisfy the VLBR requirement, temporal subsampling is a simple technique which may be combined with other compression schemes to achieve very large compression ratio. A previous proposed block-based motion compensated temporal interpolation (MCTI) (1995, 1996) can interpolate frames with reasonably good visual qualify but also with some blocking artifacts. In this paper, the block based MCTI is compared with a pixel-based approach. While the pixel-based approach has good performance in some test cases, it is highly computationally intensive making it rather unpractical. It is also found to perform poorly in some sequences.