nils-herrmann/clean_scirepeval huggingface dataset:
Rate-distortion optimized merge frame using piecewise constant functions

The ability to efficiently switch from one pre-encoded video stream to another is a valuable attribute for a variety of interactive streaming applications, such as switching among streams of the same video encoded in different bit-rates for real-time bandwidth adaptation, or view-switching among videos capturing the same dynamic 3D scene but from different viewpoints. It is well known that intra-coded I-frames can be used at switch boundaries to facilitate stream-switching. However, the size of an I-frame is large, making frequent insertion impractical. A recent proposal towards a more efficient stream-switching mechanism is distributed source coding (D-SC), which exploits worst-case correlation between a set of potential predictor frames in the decoder buffer (called side information (SI) frames) and a target frame to lower encoding rate. However, the conventional use of bit-plane and channel coding means the encoding and decoding complexity of DSC frames is large. In this paper, we pursue a novel approach to the stream-switching problem based on the concept of “signal merging”, using piecewise constant (p-wc) function as the merge operator. Specifically, we propose a new merge mode for a code block, where for each k-th transform coefficient in the block, we encode appropriate step size and horizontal shift parameters at the encoder, so that the resulting floor function at the decoder can map corresponding coefficients from any SI frame to the same reconstructed value, resulting in an identically merged signal. The selection of shift parameter per coefficient, as well as coding modes between intra and merge per block, are optimized in a rate-distortion (RD) optimal manner. Experiments show encouraging coding gain over a previous implementation of DSC frame at low-to mid-bitrates at reduced computation complexity.
Fast binary motion estimation for screen content video coding

One-bit transform (1BT), followed by binary motion estimation, is an effective alternative for accelerating traditional 8-bit motion estimation (ME) in video coding. The underlining assumption in the design of 1BT methods is that natural videos contain noise. For screen content videos, however, the special characteristics (e.g. screen content is typically noise-free) can be exploited to further improve the motion estimation accuracy. In this paper we propose a binary ME method which is specifically designed for screen content videos. In particular, binary ME is performed on a selected bit-plane. Our contribution is twofold: 1) our bit-plane selection is hardware-friendly and content-adaptive to image blocks; 2) a zero-bias early termination scheme is proposed to accelerates the binary ME procedure. Experimental results demonstrate that our proposed binary ME method improves the accuracy of obtained motion vectors for screen content video sequences.
On improving the intelligibility of synchronized over-lap-and-add (SOLA) at low TSM factor

We propose an algorithm to modify the synchronized overlap-and-add (SOLA) technique. SOLA is a popular technique for time scale modification of speech and audio signal. It changes the time scale of the signal while maintaining the pitch information. However, when the time scale of the signal is compressed more, the synthesized speech of SOLA has high articulations rate that it becomes almost impossible for the human ear to comprehend. The proposed algorithm can enhance the quality if the time scaled signal by using a time varying time scale factor which is locally adapted to the input signal. It is found that the intelligibility of the synthesized signal using the proposed algorithm is considerably better than the one synthesized from simple SOLA.
Security Analysis of Multimedia Encryption Schemes Based on Multiple Huffman Table

This letter addresses the security issues of the multimedia encryption schemes using multiple Huffman table (MHT). A known-plaintext attack is presented to show that the MHTs used for encryption should be carefully selected to avoid the weak keys problem. We then propose chosen-plaintext attacks on the basic MHT algorithm as well as the enhanced scheme with random bit insertion. In addition, we suggest two empirical criteria for Huffman table selection, based on which we can simplify the stream cipher integrated scheme, while ensuring a high level of security
Lossless/near lossless compression using bilateral filter and symmetrical structure based bias cancellation

Recently, a few symmetrical predictor structure (SPS) based lossless / near lossless compression algorithms have been proposed, which can efficiently exploit the information from the neighboring pixels. Prediction stage of existing SPS algorithms uses least squares optimization, which is computationally expensive and only causal pixels are used for the bias cancellation stage. In this paper, we propose an efficient and computationally simple lossless / near-lossless compression algorithm using the bilateral filter. Moreover we propose to use both causal and non-causal pixels for bias cancellation. From extensive experiments, it is observed that the proposed algorithm has the capability of provide better prediction and compression performance.
Improved temporal psychovisual modulation for backward-compatible stereoscopic display

Recently a new information display technique named temporal psychovisual modulation (TPVM) was developed to extend the utilities of optoelectronic displays. A typical usage of TPVM is the backward-compatible stereoscopic display, where a stereoscopic view is perceived with 3-D glasses while a 2-D view of the same scene is concurrently available for naked-eye viewers. The most challenging part of this task is to display a clean 2-D image without ghosting artifacts. In order to reduce the ghosting artifacts, we present an improved TPVM system for backward-compatible stereoscopic display. Different from previous approaches, we rebuild the TPVM system by investigating the light perception process of human eyes, and reformulate the problem as a minimization of the light intensity difference between the target and perceived views. In order to derive the light intensities, we model it as a monotonie increasing function of the pixel intensity consisting of a scaling plus an offset, and incorporate this model into our system. By solving the optimization problem, ghosting artifacts are significantly reduced in the 2-D view. Moreover, a closed-form solution is provided for real-time applications. Experimental results demonstrate that the proposed method is superior to previous approaches in terms of the 2-D view quality, while the quality of the 3-D view remains unchanged.
Fast multi-block selection for H.264 video coding

The JVT/H.264 is the new video coding standard which achieves higher coding efficiency than previous standards such as H.263 or MPEG-2. The improvement in the rate-distortion performance in H.264 is mainly due to accurate motion estimation with different mode decision including multiple block size and multiple reference frame motion estimation. 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 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 58% with similar visual quality and bit rate.
Image deblocking using convex optimization with modified total variation method

Images encoded at low bit rate usually exhibit visually annoying coding artifacts, commonly referred as the blocking artifacts. The reason for causing blocking artifacts is that each 8×8 block is quantized and encoded independently, which results in block discontinuities between neighboring blocks. In this paper, a convex optimization problem is formulated to deal with the blocking artifacts. Total variation (TV) method is introduced in this paper to impose the continuity constraint on the block boundaries. Moreover, in order to remove the staircase effect caused by the TV method, a modified TV method is proposed. Experimental results show that the proposed algorithm can remove the blocking artifacts effectively compared to other image deblocking algorithms.
Additional sign bit hiding of transform coefficients in HEVC

Transform coefficients take up a significant portion of the transmitted bitstream generated by video codec. For every non-zero coefficient, the sign information is sent separately from the amplitude. According to the statistical patterns, the signs of the transform coefficients are rarely predictable, thus bypass mode is usually used in the following CABAC engine to encode the sign information. In H.265/HEVC, a novel multiple sign bits hiding (MSBH) technique is adopted to hide one sign for each selected CG. In this paper, we propose to further perform one additional sign bit hiding (ASBH) for the selected TBs while the MSBH can coexist. To verify the effectiveness of our proposed ASBH, we implemented it on the state-of-the-art HM9.0 test model of H.265/HEVC. Experimental results show that the proposed ASBH always outperforms the anchor for all tested sequences with very trivial complexity added at the decoder side.
A blind watermarking technique for multiple watermarks

We propose a blind watermarking technique to embed multiple watermarks simultaneously. It allows the use of correlated key to embed multiple watermarks. A dual-key system is used to reduce the chance of the removal of watermark. Each embedded watermark can be decoded/detected by its own key. One direct approach and two iterative approaches are proposed to reduce the correlation effects among the noise-like keys. Experimental results show that multiple watermarks can be embedded without significant loss in PSNR and the watermark can be detected when the watermarked image is JPEG compressed.
On transformation noise as a model for correlated noise

The authors consider transformation noise processes, which are those that can be generated by passing an underlying noise process through a memoryless invertible nonlinearity. They point out the close relationship between the dependency structure of the transformation noise and that of the underlying noise. The use of the class of transformation noise generated from multivariate Gaussian background noise to approximate other noise processes is proposed. This class has tractable, closed-form joint densities. Some ad hoc ways to find the parameters in the model are suggested. It is shown that the nonlinearities in the optimal detector, in the case of transformation noise with a mixture marginal density are approximately the sum of the nominal term and a term proportional to the parameter in .<<ETX>>
On the Security of Chaotic Convolutional Coder

This paper evaluates the security of the generalized chaotic convolutional coder, which is a recently proposed joint error-correction and encryption scheme integrating the chaotic encryption into the convolutional coding. Our results show that the probability of fully recovering the pseudorandom sequence (PRS) controlling the chaotic switches is at least 0.289 under known-plaintext attack, if the number of available plaintext/ciphertext pairs p is equal to the constraint length k of the chaotic convolutional coder. In the case that p=k+e , where e ∈ Z+, we prove that the probability to fully deduce the PRS is lower bounded by 1-2-ε. Furthermore, we propose four types of chosen-plaintext attack with different decoding complexities and efficiencies to fully derive the PRS.
A robust interpolation-free approach for sub-pixel accuracy motion estimation

Motion estimation (ME) is one of the key elements in video coding standard which eliminates the temporal redundancy by using a motion vector (MV) to indicate the best match between the current frame and reference frame. A coarse to fine process is taken to find the best MV. First of all, integer-pixel ME finds a coarse MV and followed by the sub-pixel ME around the best integer-pixel point. The sub-pixel ME plays an important role in improving the coding efficiency. However, the computational complexity of searching one sub-pixel point is much higher than the integer-pixel point searching because of the interpolation and Hadamard transform operation. In this paper, an accurate optimal sub-pixel position prediction algorithm is presented. With the information of the 8 neighboring integer-pixel points, the optimal sub-pixel position is predicted directly without explicitly solving model parameters. Moreover, an outlier rejection scheme is applied to improve the robustness of the proposed algorithm. Experimental results show that the proposed algorithm outperforms the state of the art interpolation-freesub-pixel ME algorithms.
Redefining self-similarity in natural images for denoising using graph signal gradient

Image denoising is the most basic inverse imaging problem. As an under-determined problem, appropriate definition of image priors to regularize the problem is crucial. Among recent proposed priors for image denoising are: i) graph Laplacian regularizer where a given pixel patch is assumed to be smooth in the graph-signal domain; and ii) self-similarity prior where image patches are assumed to recur throughout a natural image in non-local spatial regions. In our first contribution, we demonstrate that the graph Laplacian regularizer converges to a continuous time functional counterpart, and careful selection of its features can lead to a discriminant signal prior. In our second contribution, we redefine patch self-similarity in terms of patch gradients and argue that the new definition results in a more accurate estimate of the graph Laplacian matrix, and thus better image denoising performance. Experiments show that our designed algorithm based on graph Laplacian regularizer and gradient-based self-similarity can outperform non-local means (NLM) denoising by up to 1.4 dB in PSNR.
Distributed Wireless Visual Communication With Power Distortion Optimization

This paper proposes a novel framework called DCast for distributed video coding and transmission over wireless networks, which is different from existing distributed schemes in three aspects. First, coset quantized DCT coefficients and motion data are directly delivered to the channel coding layer without syndrome or entropy coding. Second, transmission power is directly allocated to coset data and motion data according to their distributions and magnitudes without forward error correction. Third, these data are transformed by Hadamard and then directly mapped using a dense constellation (64K-QAM) for transmission without Gray coding. One of the most important properties in this framework is that the coding and transmission rate is fixed and distortion is minimized by allocating the transmission power. Thus, we further propose a power distortion optimization algorithm to replace the traditional rate distortion optimization. This framework avoids the annoying cliff effect caused by the mismatch between transmission rate and channel condition. In multicast, each user can get approximately the best quality matching its channel condition. Our experiment results show that the proposed DCast outperforms the typical solution using H.264 over 802.11 up to 8 dB in video PSNR in video broadcast. Even in video unicast, the proposed DCast is still comparable to the typical solution.
Digital watermarking using Walsh code sequences with error spreading technique and intra-pixel prediction

Digital watermarking is one solution 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. A real logo is embedded into the media with a set of Walsh code sequences (WCSs) and some error spreading techniques, such as interleaving. The logo can be extracted from a possibly corrupted image, without the help of the original uncorrupted image. The extracted logo is then corrected by intra-pixel prediction (IPP) and becomes more "readable and noticeable". The major advantage of hiding a real logo is that the logo may still be readable by human eyes even though the watermarked image is corrupted. Also, the extracted logo can be corrected by using the characteristics of images.
Bit-depth expansion by contour region reconstruction

Color bit-depth is an important attribute to image quality. However, the precision in various image capture devices limits the color bit-depth and introduces loss in visual quality. Expanding the color bit-depth is an important image enhancement issue. A good bit-depth expansion system manipulates low color bit-depth image for best visual quality as displayed on high color bit-depth monitors. However, in most color bit-depth expansion algorithms, severe contouring effect is observed in smooth gradient area which degrades the visual quality. In this paper, a novel approach is proposed. By considering the distance from contour edges, fine gradient value are applied to fill the contour gaps to achieve gradual transaction.
Bit rate and blocking artifact reduction by iterative pre-distortion

Block transform coding is widely used in image and video compression methods. Due to quantization error, there are blocking artifacts in the decoded image. This paper presents a pre-processing method, called pre-distortion, to reduce both the bit rate and blocking artifacts. The proposed algorithm is an iterative algorithm which reduces the blocking artifacts of the decoded image by introducing distortion intentionally in the encoding process. Simulation shows that, with pre-distortion, additional compression can be achieved with slight degradation in visual quality. Moreover, the blocking artifacts of the decoded image are reduced.
Dependent Joint Bit Allocation for H.264/AVC Statistical Multiplexing Using Convex Relaxation

In this paper, we address the dependent joint bit allocation problem in H.264/AVC statistical multiplexing. In most existing methods, to improve the overall visual quality, the bit allocation is based upon the instantaneous relative frame complexity of different video programs. However, due to the temporal prediction employed in H.264, the influence of a frame on the rate-distortion characteristics of the future frames should be taken into account as well. In this paper, we use our previously proposed simple, but accurate, inter-frame dependency model (IFDM) to quantitatively measure the coding dependency between the current frame and its reference frame. Based on the IFDM, we formulate the dependent joint bit allocation problem, considering both the inter-program relative frame complexity and intra-program coding dependency. We prove that the dependent joint bit allocation (DeJoBA) problem can actually be formulated and relaxed into a convex optimization problem, which can be optimally and efficiently solved. Experimental results suggest that the proposed DeJoBA method can achieve 36.81% and 13.11% bitrate reduction, on average, compared with the equal bit allocation and optimal independent joint bit allocation methods, respectively.
Motion vector coding algorithm based on adaptive template matching

Motion estimation as well as the corresponding motion compensation is a core part of modern video coding standards, which highly 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. First, a possible motion vector predictor (MVP) candidate set (CS) including several scaled spatial and temporal predictors is defined. To increase the diversity of predictors, the spatial predictor is adaptively changed based on current distribution of neighboring motion vectors. After that, adaptive template matching technique is applied to remove non-effective predictors from the CS so that the bits used for the MVP index can be significantly reduced. As the final MVP is chosen based on minimum motion vector difference criterion, a guessing strategy is further introduced so that in some situations the bits consumed by signaling the MVP index to the decoder can be totally omitted. The experimental results indicate that the proposed method can achieve an average bit rate reduction of 5.9% compared with the H.264 standard.
An Analytic Framework for Frame-Level Dependent Bit Allocation in Hybrid Video Coding

In this paper, we address the frame-level dependent bit allocation (DBA) problem in hybrid video coding. In most existing methods, the DBA solution is achieved at the expense of high, sometimes even unbearable, computational complexity because of the multipass coding involved. Motivated by this, we propose a model-based approach as an attempt to solve this problem analytically. Leveraging the predictive nature in hybrid video coding, we develop a novel interframe dependency model (IFDM), which enables a quantitative measure of the coding dependency between the current frame and its reference frame. Based on the IFDM, the buffer-constrained frame-level DBA problem is carefully formulated. Finally, the model-based DBA method called IFDM-DBA is derived, in which successive convex approximation techniques are employed to convert the original optimization problem into a series of convex optimization problem s of which the optimal solutions can be obtained efficiently. Experimental results suggest that the proposed IFDM-DBA method can achieve up to a 23% bitrate reduction over the JM reference software of H.264.
Piecewise linear model for real-time rate control [video coding applications]

Most existing rate control schemes in the literature evaluate quantization parameters of macro-blocks (MB) based on models which estimate well at low bit rates (e.g. quadratic model). These models are not flexible because this model does not estimate well for different situations with different rates. This work investigates the relationships of the rate and the distortion of a residue MB with two parameters - MB quantization step size and standard deviation of MB prediction errors. A piecewise linear rate model and linear distortion model are proposed. Then, we present the proposed rate control scheme based on these models. The experimental results suggest that our scheme achieves PSNR gain over TMN8 and Li's scheme.
Subpixel-based image downsampling-some analysis and observation

Often we need to shrink a high resolution image (e.g. 10-mega pixel) in order to display it on a low resolution display (e.g mobile phone). Signal processing theory tells us that optimal decimation requires low-pass filtering with a suitable cutoff frequency followed by downsampling. In doing so, we need to remove lots of details in the original high resolution image. In this paper, we review some little known results on an interesting topic called subpixel rendering, which can provide apparent higher resolution at the expense of color fringing artifacts. We attempt to explain what happens and why this is even possible.
On a Conjecture on Error Recovery for Variable Length Codes

Maxted et al. in 1985 gave a conjecture stating that, for a Geometric source, the stable code has the best error recovery performance for the case of bit inversion among all Huffman codes for this source, while the unstable code has the worst error recovery performance. This conjecture was extended by Swaszek et al. ten years later, but without proof, to sources with certain probability mass function. In this paper, we prove the correctness of the extended version of this conjecture. Our proof provides a novel mathematical technique for proving the optimality of variable length code in the sense of error recovery capability. Furthermore, our result offers some insight into the working mechanism of the suffix condition that has been widely used by many heuristic algorithms to find error-resilient codes.