nils-herrmann/clean_scirepeval huggingface dataset:
Frequency selection and merging with universal matrices for color filter array demosaicking

In most digital cameras, color filter array (CFA) is used for sampling only one color value for each pixel on CCD. So the other two color values need to be interpolated. The interpolation process is commonly known as demosaicking. In this paper, we discuss the relationship among the down sampled CFA images in frequency domain. According to the correlation, we propose an interpolation method that select specified parts from every down sampled frequency CFA image and merge them together. For more accurate selection, we calculate the minimum error of points in frequency images, and produce some universal matrices to record the source of every point in merging. And then, interpolation is based on the universal matrices. This algorithm is a non-adaptive method and regular in hardware implementation.
Image Interpolation Using Autoregressive Model and Gauss-Seidel Optimization

In this paper we propose a simple yet effective image interpolation algorithm based on autoregressive model. Unlike existing algorithms which rely on low resolution pixels to estimate interpolation coefficients, we optimize the interpolation coefficients and high resolution pixel values jointly from one optimization problem. Although the two sets of variables are coupled in the cost function, the problem can be effectively solved using Gauss-Seidel method. We prove the iterations are guaranteed to converge. Experiments show that on average we have over 3dB gain compared to bicubic interpolation and over 0.1dB gain compared to SAI.
Symmetrical predictor structure based integrated lossy, near lossless/lossless coding of images

Prediction based algorithms reported in the literature are not able to integrate lossy and near-lossless/lossless coding and uses only causal pixels (non-symmetrical predictor structure) for prediction. A non-symmetrical predictor structure, however, is not able to efficiently adapt near the intensity varying areas, which results into poor prediction. Hence, we propose a novel two-stage algorithm for lossy, near lossless/lossless compression using a symmetrical predictor structure is proposed. In the first stage, the proposed algorithm encodes and decodes the given image using the JPEG-2000 standard algorithm (lossy coding). This JPEG-2000 decoded image in the first stage, enables us to use the symmetrical predictor (using both causal and non-causal pixels) for prediction in the second stage. A performance evaluation shows that our algorithm is significantly better in terms of compression performance as compared to some of the computationally complex methods.
A multi-hypothesis decoder for multiple description video coding

Multiple description coding (MDC) can be used as an error resilience (ER) technique for video coding. In case of transmission errors, error concealment (EC) 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 novel algorithm based on a multi-hypothesis decoder (MHD), to improve the reconstructed video quality of MDC over packet loss networks. Both subjective and objective results show that MHD can help to achieve a better video quality than a traditional EC algorithm.
Highly Parallel Rate-Distortion Optimized Intra-Mode Decision on Multicore Graphics Processors

Rate-distortion (RD)-based mode selections are important techniques in video coding. In these methods, an encoder may compute the RD costs for all the possible coding modes, and select the one which achieves the best trade-off between encoding rate and compression distortion. Previous papers have demonstrated that RD-based mode selections can lead to significant improvements in coding efficiency. RD-based mode selections, however, would incur considerable increases in encoding complexity, since these methods require computing the RD costs for numerous candidate coding modes. In this paper, we consider the scenario where software-based video encoding is performed on personal computers or game consoles, and investigate how multicore graphics processing units (GPUs) may be efficiently utilized to undertake the task of RD optimized intra-prediction mode selections in audio and video coding standards and H.264 video encoding. Achieving efficient GPU-based intra-mode decisions, however, could be nontrivial for two reasons. First, intra-mode decision tends to be sequential. Specifically, the mode decision of the current block would depend on the reconstructed data of the neighboring blocks. Therefore, the coding modes of neighboring blocks would need to be computed first before that of the current block can be determined. This dependency poses challenges to GPU-based computation, which relies heavily on parallel data processing to achieve superior speedups. Second, RD-based intra-mode decision may require conditional branchings to determine the encoding bit-rate, and these branching operations may incur substantial performance penalties when being executed on GPUs due to pipeline architectural designs. To address these issues, we analyze the data dependency in intra-mode decision, and propose novel greedy-based encoding orders to achieve highly parallel processing of data blocks. We also prove that the proposed greedy-based orders are optimal in our problem, i.e., they require the minimum number of iterations to process a video frame given the dependency constraints. In addition, we propose a method to estimate the coding rate suitable for GPU implementation. Experimental results suggest our proposed solution can be more than 50 times faster than the previously proposed parallel intra-prediction, since our work can efficiently exploit the massive parallel opportunity in GPUs.
A robust spatial-temporal line-warping based deinterlacing method

In this paper, a line-warping based deinterlacing method will be introduced. The missing pixels in interlaced videos can be derived from the warping of pixels in horizontal line pairs. In order to increase the accuracy of temporal prediction, multiple temporal-line pairs, selected according to constant velocity model, are used for warping. The stationary pixels can be well-preserved by accuracy stationary detection. A soft switching between spatial-temporal interpolated values and temporal average is introduced in order to prevent unstable switching. Owing to above novelties, the proposed method can yield higher visual quality deinterlaced videos than conventional methods. Moreover, this method can suppress most deinterlaced visual artifacts, such as line-crawling, flickering and ghost-shadow
Integration of Recursive Temporal LMMSE Denoising Filter Into Video Codec

The presence of noise can dramatically affect the efficiency of video compression systems. For performance improvement, most practical video compression systems adopt a denoising filter as a pre-processing module for the video encoder, or as a post-processing module for the video decoder, but the complexity introduced by denoising can be very high. This paper first presents a recursive temporal linear minimum mean squared error (LMMSE) filter for video denoising. Based on the analysis of the hybrid video compression process, two novel schemes are presented, one for video encoding and the other for video decoding, in which the proposed recursive temporal LMMSE filter is seamlessly integrated into the encoding and the decoding processes, respectively. For both of these two schemes, the denoising is implemented with nearly no extra computation introduced. Experimental results validate the effectiveness of the proposed schemes on encoding and decoding noisy video sequences.
Precision Enhancement of 3-D Surfaces from Compressed Multiview Depth Maps

Transmitting depth maps captured from multiple viewpoints of a 3-D scene enables a wide range of receiver-side 3-D applications, including virtual view synthesis via depth-image-based rendering (DIBR). Observing that compressed depth maps from different viewpoints constitute multiple descriptions (MD) of the same signal, we propose to reconstruct 3-D surfaces of the scene by considering multiple compressed depth maps jointly. Specifically, we propose an alternating projection algorithm, inspired by the theory of projection onto convex sets (POCS), which at convergence returns a 3-D surface that satisfies three sets of conditions: spatial smoothness prior, quantization bin constraints in the block transform domain, and inter-view consistency. We present a theoretical proof that shows convergence of our algorithm under benign conditions. Compared to existing multiview depth map denoising schemes and single image de-quantization schemes, our proposed solution achieves higher objective quality for both reconstructed depth maps and synthesized virtual views.
Solving dense stereo matching via quadratic programming

We study the problem of formulating the discrete dense stereo matching using continuous convex optimization. One of the previous work derived a relaxed convex formulation by establishing the relationship between the disparity vector and a warping matrix. However it suffers from high computational complexity. In this paper, the previous convex formulation is translated into an equivalent quadratic programming (QP). Then redundant variables and constraints are eliminated by exploiting the internal sparse property of the warping matrix. The resulting QP can be efficiently tackled using interior point solvers. Moreover, enhanced smoothness term and effective post-processing procedures are also incorporated to further improve the disparity accuracy. Experimental results show that the proposed method is much faster and better than the previous convex formulation, and provides competitive results against existing convex approaches.
Maximum-likelihood versus maximum a posteriori based local illumination and color correction algorithm for multi-view video

In multi-view video, illumination and color inconsistency among different views always exist because of imperfect camera calibration, CCD noise, camera positions and orientations, etc. Since illumination and color inconsistency greatly reduce the coding efficiency and rendering quality of multiview video, effective illumination and color correction modules are necessary for practical multi-view video processing system. In this paper, we proposed two local illumination and color correction algorithms. In these two algorithms, the correction matrix is estimated by applying maximum likelihood (ML) and maximum a posteriori (MAP) methods respectively. According to the Bayes rule, the MAP estimate is determined by two terms: error conditional density model (likelihood model)and priori conditional density model. Experimental results show that both the ML and MAP based correction matrices greatly improve the illumination and color consistency among different views. Moreover, images corrected by MAP based correction matrix look much nicer than those corrected by ML based correction matrix.
On the design of an efficient encryption-then-compression system

In many practical scenarios, image encryption has to be conducted prior to image compression. This has led to the problem of how to design a pair of encryption and compression algorithms such that compressing the encrypted image can still be efficiently performed. In this work, we propose a permutation-based image encryption method conducted over the prediction error domain. We also design an arithmetic coding (AC)-based approach to efficiently compress the encrypted image. It can be shown that the proposed scheme can provide reasonably high level of security. More notably, the compression performance on the encrypted image is only slightly degraded, compared with that of compressing the original, un-encrypted one. In contrast, most of the existing approaches induce significant penalty on the compression performance.
How anti-aliasing filter affects image contrast: An analysis from majorization theory perspective

When we design an anti-aliasing low pass filter, it is usually an IIR filter. We need to truncate the filter to an FIR filter. One may think that the more taps there are, the better the image quality is. However, we find that there exists an optimal value of tap number that will give the best visual quality. Filters with larger or smaller number of taps will degrade the image quality, due to the fact that the image contrast is reduced. In this paper we analyze this phenomenon using majorization theory and find that the image contrast can be formulated as a Schur convex function on filter coefficients. We also propose an effective method to choose the best filter so that the image contrast is maximized, so as to give best visual quality.
Intra mode dependent quantization error estimation of each DCT coefficient in H.264/AVC

H.264/AVC employs intra prediction to reduce spatial redundancy between neighboring blocks. Different directional prediction modes are used to cater diversified video content. Although it achieves quite high coding efficiency, it is desirable to establish a proper theoretical quantization error model under different prediction modes, since this allows us to explain the behavior of existing codecs and to design better ones. Actually, residue after different intra prediction modes exhibits different characteristics in frequency domain. In this paper, an intra mode dependent quantization error estimation is presented. For a complete analysis, we investigate not only the coding distortion in current JM reference software, but also its effect on intra mode dependent residue. Based on the mode dependent residue characteristics, a mode dependent quantization error estimation for each frequency position is proposed. Simulation results show that the proposed model can estimate the mode dependent quantization error with high accuracy. Furthermore, the estimation accuracy remains high for various sequences and QP.
An efficient motion vector coding algorithm based on adaptive predictor selection

Motion Estimation is a core part of modern video coding standards, which significantly improves the compression efficiency. On the other hand, motion information takes considerable portion of compressed bit stream, especially in low bit rate situation. In this paper, an efficient motion vector prediction algorithm is proposed to minimize the bits used for coding the motion information. Several spatial and temporal neighboring motion vectors are selected as the motion vector predictor (MVP) candidates. By applying template matching to each block, a near-optimal MVP can be obtained both at the encoder and decoder side, thus no predictor index is needed to signal to the decoder. We also embed the MVP into current motion estimation process. Furthermore, a correction technique is executed as a remedy when template matching picks out a non-efficient predictor. Simulation results indicate that a bit rate reduction of up to 7.29% over H.264/AVC is achieved by the proposed scheme.
A novel multiple description video coding based on H.264/AVC video coding standard

Multiple description coding (MDC) is a source coding technique that exploits path diversity to solve packet losses over error-prone channels. In this paper, we propose an improved drift-free multi-state MDC method based on H.264/AVC coding scheme. At the encoder side, we compress original video into multiple independent H.264 streams with different coding parameters, which can help us to control correlations between the descriptions. At the decoder side, each description is considered as a noisy observation of the original video, and a linear minimum mean square error (LMMSE) based merge algorithm is proposed to combine the descriptions. Experimental results show that the proposed algorithm can achieve better coding efficiency and visual quality than temporary MDC present in . The error resilience ability is also improved by the fact that each frame is coded twice with different parameters.
A new adaptive subpixel-based downsampling scheme using edge detection

In this paper, a new adaptive subpixel-based downsampling scheme is proposed. Inside this scheme, we take full advantage of subpixels by adaptively choosing the sample directions based on edge information which has not been addressed before. Then, an adaptive filter is designed to suppress color fringing artifacts. Moreover, a good cut-off frequency is derived and deployed in our filter to obtain extra information. Simulation results illustrate that the proposed adaptive subpixel-based downsampling scheme successfully improves the resolution while efficiently removes visible color fringing artifacts.
Low color bit-depth image enhancement by contour-region dithering

In low-end imaging systems which adopt low pixel precision, color bit-depth of an image is limited to a few bits. As a result, contouring effect tends to occur in smooth regions leading to annoying visual artifacts. Besides, the output color bit-depth is sometimes restricted in low-end systems. To enhance the visual quality in low-end imaging systems, contouring artifacts should be concealed as much as possible under the system restrictions. In this paper, a comprehensive method is proposed which combines color bit-depth expansion and dithering to enhance the image quality.
Security evaluation of a perceptual image hashing scheme based on virtual watermark detection

This paper evaluates the security of a recently proposed perceptual image hashing scheme based on virtual watermark detection. Under the known-hash attack where the attacker has access to several image/hash vector pairs, we show that the task of estimating the virtual watermark sequences serving as the secret key can be formulated as a simple convex optimization problem, and hence, can be solved efficiently. More specifically, we demonstrate that satisfactory level of estimation accuracy of a watermark sequence of length m could be achieved from approximately 2 · m image/hash vector pairs on average. Experimental results using artificial data and real image data are also provided to verify the effectiveness of our proposed attack approaches.
A novel post-processing method for H.264/AVC video quality enhancement

Lossy compression has been widely used to reduce the bandwidth for image/video transmission or storage, especially in networks in which high compression ratio is often expected. However, the coded video suffers from different degree of distortions. Post-processing can reduce these distortions and alleviate the conflict between bit rate reduction and quality preservation. In this paper we propose a linear minimum mean square error (LMMSE) based post-processing algorithm in DCT domain to enhance the video quality after compression. We treat the reconstructed DCT coefficients before deblocking and after deblocking as two noisy observations of the DCT coefficients of the original frame, and try to estimate the original ones by merging the two observations optimally. Experimental results demonstrate that our proposed algorithm can efficiently improve the video quality by about
Fast algorithm of arbitrary factor subpixel downsampling based on frequency analysis

Subpixel-based downsampling has shown its advantages over pixel-based downsampling in terms of preserving more spatial details along edges and generating sharper images, at the cost of certain amount of color-fringing artifacts in the downsampled image. To balance the sharpness and color-fringing artifacts, some algorithms are proposed to design optimal anti-aliasing (AA) filters, which are either image independent, or computationally too expensive. And all of the existing AA filters are designed for fixed downsampling factor, which makes them impractical for real applications. In this paper we propose two fast algorithms to design AA filter for arbitrary factor subpixel downsampling based on frequency analysis of the input image. The proposed algorithms generate image dependent AA filter which is as good as the state-of-the-art algorithm, but much faster.
Lossless Visible Watermarking

The embedding distortion of visible watermarking is usually larger than that of invisible watermarking. In order to maintain the signal fidelity after the watermark extraction, "lossless" property is highlighted in the visible watermarking. In this paper, we propose two lossless visible watermarking algorithms, pixel value matching algorithm (PVMA) and pixel position shift algorithm (PPSA). PVMA uses the bijective intensity mapping function to watermark a visible logo whereas PPSA uses circular pixel shift to improve the visibility of the watermark in the high variance region. For the application of medical and military, as they are sensitive to distortion, PVMA and PPSA can be used to insert a visible logo to prevent unauthorized use
Edge-based predictive scanning scheme of DCT coefficients for inter-frame video coding

Scanning of quantized transform coefficients is a very significant procedure in video coding. In H.264, it affects the coding efficiency of the following CABAC or CAVLC entropy coder directly. In this paper, we propose a novel edge-based predictive scanning scheme to improve the coding efficiency for inter-frame coding. This scheme includes three scanning pattern candidates. Besides zigzag pattern as defined in H.264, two alternative patterns are obtained by on-line training on frame level. Specifically, reference block is utilized to predict edge information in current 8×8 block. Based on predictive edge information, a suitable scanning pattern will be selected to scan the quantized coefficients of current 8×8 block. Since a similar prediction process can be done in the decoder side as well, no overhead is needed to be transmitted in the bit-stream. Experimental results show that the proposed edge-based predictive scanning scheme yields an average of 0.42% BD-bitrate reduction over the H.264 high profile.
Optimal key frame selection using visual content metric

Key frame selection is the process to select the most representative frames of a long video sequence based on a given set of rules. This paper presents a novel algorithm for selecting a set of representative frames in MPEG compressed video. We use a luminance DCT DC value, macroblock type and motion vector information to determine the motion activity and visual content change in MPEG compressed video. The advantages of this algorithm are highly accurate video content description and efficient extraction of the required information in the MPEG compressed domain by low complexity inverse quantization and variable length decoding. A visual content based key frame selection is proposed. One of the advantages of this approach is that it does not depend on any predefined threshold or parameter, It gives a set of key frames based on an objective model of visual content flow. Another advantage is that the predefined number of key frames can be obtained by using the proposed algorithm.
Fast intra-prediction mode selection for 4A blocks in H.264

In the upcoming H.264 advanced video coding standard, intra-prediction for each 4/spl times/4 block is used to compress I-frames. However, the full search algorithm to choose one of the 9 prediction modes is computationally expensive. We propose a fast intra-prediction mode selection (FIPMS) method based on partial computation of the cost function, early termination and selective computation of highly probable modes. The proposed FIPMS can reduce the complexity considerably while maintaining similar PSNR and bit rate.
Block based parallel motion estimation using programmable graphics hardware

In this paper, we propose a graphics processing unit (GPU)-based motion estimation for H.264/AVC by rearranging the encoding order of 4times4 blocks. Previous research by Kelly and Kokaram (2003) and Lin et al. (2006) assume no dependency among adjacent blocks, which is not true for H.264/AVC. The proposed method not only overcomes the dependency problem, but also can be used for both full search and fast search. Experimental results show that our implementation is forty five faster than our SDVTD optimized CPU implementation.