nils-herrmann/clean_scirepeval huggingface dataset:
Fast time scale modification using envelope-matching technique (EM-TSM)

Time scaling of speech and audio signals is one of the key features of the upcoming MPEG4 standard. Synchronized Overlap-and-Add (SOLA) is a time scaling algorithm known to achieve good speech quality. One problem of SOLA is that it requires a large amount of computation in the search of the best matching point between the analysis and synthesis frames. This is especially serious when multiple time scaling factors are to be supported as in the case of MPEG4. In this paper, we propose a technique called envelop-matching to simplify the computation with effectively the same quality. In envelop-matching, zero crossing locations are used as features for the search. We propose a very fast algorithm for the distortion computation.
Advanced Macro-block Entropy Coding in H.264

Existing video encoders, such as MPEG4, H.263 and H.264, adopt variable length coding (VLC) as entropy coding method, focusing on residue data coding. In low bit-rate video coding, larger quantization parameters are used to give a smaller number of bits spent on residue data. As a result, the header overhead is a dominant factor of yielding the overall bit rates. In this paper, macro-block (MB) header behavior is investigated. It is found that the relative percentage of the number of bits of MB header increases with quantization parameter (QP) and header correlation between neighboring MBs is high. Based on these MB header properties, we propose an advanced coding algorithm for entropy coding of MB header. The experimental results suggest that our proposed algorithm achieves lower total number of encoded bits over JM10.2, up to 10% bit reduction, with the same PSNR quality. At the same bit rates, our algorithm has about 0.3-0.4 dB PSNR gain.
Block-Based Lossless Data Hiding in Delta Domain

Digital watermarking is one of the ways to prove the ownership and the authenticity of the media. However, 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 in delta domain. A MSE discount is obtained by using checkerboard-pattern watermark sequences. The PSNR between the watermarked image and the original image is high and there is no "salt-and-peppers" artifact. The proposed algorithm can be extended to withstand the JPEG attack
Similar images compression based on DCT pyramid multi-level low frequency template

Medical imaging applications produce a huge amount of similar images. Instead of compressing each image individually, set redundancy compression (SRC) methods remove the inter image redundancy and reduce storage. However, in the previous SRC methods - MMD, MMP and Centroid methods, the prediction templates for extracting set redundancy are not very efficient, especially when image sets are very large with several clusters. In this paper, inspired by face recognition techniques, a novel lossless SRC method is derived based onDCT pyramid multi-level low frequency template. The approximation subband is used as a prediction template for each image to calculate the residue. Intra prediction is also used to reduce the entropy of the residues. Experiments with 3 sets of MR brain images demonstrate the efficiency of our proposed algorithm in respect to bits/pixel (bpp).
Reducing computational complexity of dynamic time warping-based isolated word recognition with time scale modification

In this paper, we propose an algorithm to reduce the computational complexity of dynamic time warping for isolated speech recognition. Prior to the feature extraction process in speech recognition, we apply time scale compression both on the reference and testing utterances. Experimental results show that the computational complexity can be reduced by up to 75% without affecting the accuracy of recognition. The time scale process can also suppress the effect of noise to a certain degree so that the recognition accuracy can be improved for noisy test utterances. The proposed algorithm can solve the problem of high mismatch of utterance duration between two utterances.
Multiresolution Graph Fourier Transform for Compression of Piecewise Smooth Images

Piecewise smooth (PWS) images (e.g., depth maps or animation images) contain unique signal characteristics such as sharp object boundaries and slowly varying interior surfaces. Leveraging on recent advances in graph signal processing, in this paper, we propose to compress the PWS images using suitable graph Fourier transforms (GFTs) to minimize the total signal representation cost of each pixel block, considering both the sparsity of the signal's transform coefficients and the compactness of transform description. Unlike fixed transforms, such as the discrete cosine transform, we can adapt GFT to a particular class of pixel blocks. In particular, we select one among a defined search space of GFTs to minimize total representation cost via our proposed algorithms, leveraging on graph optimization techniques, such as spectral clustering and minimum graph cuts. Furthermore, for practical implementation of GFT, we introduce two techniques to reduce computation complexity. First, at the encoder, we low-pass filter and downsample a high-resolution (HR) pixel block to obtain a low-resolution (LR) one, so that a LR-GFT can be employed. At the decoder, upsampling and interpolation are performed adaptively along HR boundaries coded using arithmetic edge coding, so that sharp object boundaries can be well preserved. Second, instead of computing GFT from a graph in real-time via eigen-decomposition, the most popular LR-GFTs are pre-computed and stored in a table for lookup during encoding and decoding. Using depth maps and computer-graphics images as examples of the PWS images, experimental results show that our proposed multiresolution-GFT scheme outperforms H.264 intra by 6.8 dB on average in peak signal-to-noise ratio at the same bit rate.
Video decoder embedded with temporal LMMSE denoising filter

Under noisy circumstance, the encoded video can be corrupted by noise and the decoded video may be very noisy. In this paper, a novel scheme is proposed for decoding noisy video bitstreams. First a temporal linear minimum mean squared error (LMMSE) estimator for video denoising is presented. Based on the analysis of hybrid video decoder, this temporal LMMSE denoising Altering is seamlessly embedded into hybrid video decoding process. The operations performed for denoising are very few, and the total complexity of the proposed scheme is very similar to that of a regular decoder. Experimental results show that compared to the standard decoder, with the proposed scheme, the subjective quality of the decoded video can be significantly improved.
An Encoder-Embedded Video Denoising Filter Based on the Temporal LMMSE Estimator

Noise not only degrades the visual quality of video contents, but also significantly affects the coding efficiency. Based on the temporal linear minimum mean square error (LMMSE) estimator, an innovative denoising filter is proposed in this paper. The proposed filter only requires simple operations manipulating on the individual residue coefficients and can be seamlessly integrated into video encoders. Compared to traditional filter-encoder cascaded scheme, embedding the proposed filter into the video encoder can save a large amount of computation. The experimental results show that with the proposed filter embedded, both the noise suppression capability and the coding efficiency of the video encoder can be dramatically improved. Furthermore, as a purely temporal filter, it can well preserve the fine details of video contents and satisfactory visual quality can be achieved
A capacity estimation technique for JPEG-to-JPEG image watermarking

In JPEG-to-JPEG image watermarking (J2J), the input is a JPEG image file. After watermark embedding, the image is JPEG-compressed such that the output file is also a JPEG file. We use the human visual system (HVS) model to estimate the J2J data hiding capacity of JPEG images, or the maximum number of bits that can be embedded in JPEG-compressed images.  Watson's HVS model (Proc. SPIE Human Vision, Visual Process., and Digital Display IV, p.202-16, 1993) is modified to estimate the just noticeable difference (JND) for DCT coefficients. The number of modifications to DCT coefficients is limited by JND in order to guarantee the invisibility of the watermark. Our capacity estimation method does not assume any specific watermarking method and thus would apply to any watermarking method in the J2J framework.
Image compression via sparse reconstruction

The traditional compression system only considers the statistical redundancy of images. Recent compression works exploit the visual redundancy of images to further improve the coding efficiency. However, the existing works only provide suboptimal visual redundancy removal schemes. In this paper, we propose an efficient image compression scheme based on the selection and reconstruction of the visual redundancy. The visual redundancy in an image is defined by some images blocks, named redundant blocks, which can be well reconstructed by the others in the image. At the encoder, we design an effective optimization strategy to elaborately select redundant blocks and intentionally remove them. At the decoder, we propose an image restoration method to reconstruct the removed redundant blocks with minimum reconstructed error. Encouraging experimental results show that our compression scheme achieves up to 13.67% bit rate reduction with a comparable visual quality compared to traditional High Efficiency Video Coding (HEVC).
Adaptive Bilateral Filter Considering Local Characteristics

In this paper, we propose a simple but effective adaptive bilateral filter considering local characteristics. The presented method exploits the local gaussian gradient information of the processing image and applies bilateral filter with changing the range filter parameter $\sigma_{r}$ adaptively. The proposed adaptive bilateral filter could preserve more details of the image compared with original bilateral filter in edge or texture regions. What is more, it is more robust to the noise. Also compared with the original bilateral filter and the state-of-art wavelet de-noising method, the proposed method also can effectively remove signal noise while preserving the original structure.
Fast browsing of speech material for digital library and distance learning

In digital library and distance learning applications, one usually needs to search through lots of speech material. While content-based retrieval techniques can help to identify possible matching items, the person would usually need to browse through the items quickly before making decisions on whether the items are useful or not. As a result, fast speech browsing techniques are highly desirable. In this paper we discuss problems of fast playback of speech materials and overview some existing time scale modification (TSM) techniques. We propose some novel modifications of TSM to make it much more effective in fast browsing of speech materials, especially those with irregular speech tempo. The proposed algorithm includes silent period removal, gain equalization and locally adaptive TSM. Simulation results show that the proposed algorithm can increase the intelligibility of the fast playback speech materials significantly.
Joint security and performance enhancement for secure arithmetic coding

This paper studies the joint security and performance enhancement of secure arithmetic coding (AC) for digital rights management applications. The proposed cryptosystem incorporates the interval splitting AC with a simple bit-wise XOR operation step. Security analysis results show that the proposed scheme provides satisfactory level of security against the cipher-only attack, the chosen-plaintext attack and the chosen-ciphertext attack. Due to the elimination of the input symbol-wise permutation step, our proposed scheme can be extended conveniently to any context-based coding scenarios. In addition, the implementation complexity of our proposed scheme is lower than the original secure AC. Finally, we suggest a selective encryption version of our proposed scheme, which further reduces the implementation complexity.
Stereo Matching with Optimal Local Adaptive Radiometric Compensation

A common assumption in stereo matching is that the corresponding pixels in stereo images have similar pixel values. Unfortunately, such an assumption may not be true due to radiometric variations in different views, leading to severely degraded matching results. In this letter, we propose a radiometrically invariant stereo matching algorithm called Optimal Local Adaptive Radiometric Compensation (LARAC). In LARAC, we approximate the spatially varying Pixel Value Correspondence Function (PVCF) between a corresponding pixel pair as a locally consistent polynomial within an optimal local adaptive window. The optimal polynomial coefficients are obtained for each candidate disparity value and are used to compute the matching cost. Meanwhile, a self-correction property is achieved by the proposed LARAC, leading to reduced matching errors for the outlier pixels. Experimental results suggest that the proposed LARAC outperforms other state-of-the-art stereo matching algorithms.
Embedded denoising for the H.264/AVC extension-spatial SVC

Recently, many schemes that embed the denoising process into the video encoding have been proposed. They are mainly implemented into the single-layer encoder, like the H.264 reference encoder JointModel(JM). In this paper, we propose to embed the denoising process into the multi-layer spatial SVC encoder. Since either for the base layer or the enhancement layer our proposed filter could achieve Linear Minimum Mean Square Error(LMMSE) and meanwhile the additional inter-layer prediction tools could be utilized, the encoder performance can be largely improved. The experimental results show that the multi-layer embedded approach can remove most of the noise while achieving spatial scalability. And the R-D curve shows the optional use of inter-layer prediction tools can help yield better denoising performance and coding efficiency.
Data hiding in error diffused color halftone images

Halftone image watermarking has been explored and developed rapidly over the past decade. However, there are still issues to be studied. This paper presents a data hiding method called Data Hiding by Dual Color Conjugate Error Diffusion (DHDCCED) to hide a binary secret pattern into two error diffused color halftone images, such that when the two color halftone images are overlaid, the secret pattern will be revealed. The experimental results show that DHDCCED can significantly improve the performances when comparing both the correct decoding rate and the visual quality of the revealed secret pattern to the existing method Color Conjugate Error Diffusion (CCED).
Bit-depth expansion using Minimum Risk Based Classification

Bit-depth expansion is an art of converting low bit-depth image into high bit-depth image. Bit-depth of an image represents the number of bits required to represent an intensity value of the image. Bit-depth expansion is an important field since it directly affects the display quality. In this paper, we propose a novel method for bit-depth expansion which uses Minimum Risk Based Classification to create high bit-depth image. Blurring and other annoying artifacts are lowered in this method. Our method gives better objective (PSNR) and superior visual quality as compared to recently developed bit-depth expansion algorithms.
Antialiasing Filter Design for Subpixel Downsampling via Frequency-Domain Analysis

In this paper, we are concerned with image downsampling using subpixel techniques to achieve superior sharpness for small liquid crystal displays (LCDs). Such a problem exists when a high-resolution image or video is to be displayed on low-resolution display terminals. Limited by the low-resolution display, we have to shrink the image. 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 many useful image details causing blurring. Subpixel-based downsampling, taking advantage of the fact that each pixel on a color LCD is actually composed of individual red, green, and blue subpixel stripes, can provide apparent higher resolution. In this paper, we use frequency-domain analysis to explain what happens in subpixel-based downsampling and why it is possible to achieve a higher apparent resolution. According to our frequency-domain analysis and observation, the cutoff frequency of the low-pass filter for subpixel-based decimation can be effectively extended beyond the Nyquist frequency using a novel antialiasing filter. Applying the proposed filters to two existing subpixel downsampling schemes called direct subpixel-based downsampling (DSD) and diagonal DSD (DDSD), we obtain two improved schemes, i.e., DSD based on frequency-domain analysis (DSD-FA) and DDSD based on frequency-domain analysis (DDSD-FA). Experimental results verify that the proposed DSD-FA and DDSD-FA can provide superior results, compared with existing subpixel or pixel-based downsampling methods.
Optimal graph laplacian regularization for natural image denoising

Image denoising is an under-determined problem, and hence it is important to define appropriate image priors for regularization. One recent popular prior is the graph Laplacian regularizer, where a given pixel patch is assumed to be smooth in the graph-signal domain. The strength and direction of the resulting graph-based filter are computed from the graph's edge weights. In this paper, we derive the optimal edge weights for local graph-based filtering using gradient estimates from non-local pixel patches that are self-similar. To analyze the effects of the gradient estimates on the graph Laplacian regularizer, we first show theoretically that, given graph-signal hD is a set of discrete samples on continuous function h(x; y) in a closed region Ω, graph Laplacian regularizer (hD)TLhD converges to a continuous functional SΩ integrating gradient norm of h in metric space G-i.e., (∇h)TG-1(∇h)-over Ω. We then derive the optimal metric space G*: one that leads to a graph Laplacian regularizer that is discriminant when the gradient estimates are accurate, and robust when the gradient estimates are noisy. Finally, having derived G* we compute the corresponding edge weights to define the Laplacian L used for filtering. Experimental results show that our image denoising algorithm using the per-patch optimal metric space G* outperforms non-local means (NLM) by up to 1.5 dB in PSNR.
Wyner-Ziv Successive Refinement of Video and Rate Distortion Analysis

In Wyner-Ziv video coding system, motion estimation efficiency is much lower than that in conventional video coding system because current frame is not available when doing motion estimation. In this paper, we propose a successive resolution refinement algorithm to improve motion estimation efficiency. Based on our rate distortion analysis, we derive optimal down-sample ratio for two-stage successive resolution refinement system. We also analyze the performance of multistage case, and find that it approaches the performance of ideal motion compensated Wyner-Ziv video coding system, with at most 2.17 dB loss in PSNR. Experimental results demonstrate the correctness of the analysis and show that the proposed method out-performs original bit-plane refinement scheme up to 2.5 dB, with much lower complexity.
A low power and standard-compliant RDO motion estimation hardware architecture for VBSME

Motion Estimation (ME) is the most computationally intensive part in the whole video compression process. The ME algorithms can be divided into full search ME (FS) and fast ME (FME). The FS is not suitable for high definition (HD) frame size videos because its relevant high computation load and hard to deal with complex motions in limited search range. A lot of FME algorithms have been proposed which can significantly reduce the computation load compared to FS. Though many kinds of hardware implementations of ME have been proposed, almost all of them fail to consider about the motion vector field (MVF) coherence and rate-distortion (RD) cost which have significant impact to the coding efficiency. In this paper, we propose a novel hardware-oriented motion estimation algorithm called RD Optimized single-MVP-biased FS (RDOMFS), and corresponding highly data reusable hardware architecture. Simulation results show that the proposed ME algorithm performs better RD performance than conventional FME algorithm. The design is implemented with TSMC 0.13um CMOS technology and costs 103k gates. At a clock frequency of 61 MHz, the architecture achieves real-time 1920 × 1080 RDO-VBSME at 30fps.
Depth map compression using multi-resolution graph-based transform for depth-image-based rendering

Depth map compression is important for efficient network transmission of 3D visual data in texture-plus-depth format, where the observer can synthesize an image of a freely chosen viewpoint via depth-image-based rendering (DIBR) using received neighboring texture and depth maps as anchors. Unlike texture maps, depth maps exhibit unique characteristics like smooth interior surfaces and sharp edges that can be exploited for coding gain. In this paper, we propose a multi-resolution approach to depth map compression using previously proposed graph-based transform (GBT). The key idea is to treat smooth surfaces and sharp edges of large code blocks separately and encode them in different resolutions: encode edges in original high resolution (HR) to preserve sharpness, and encode smooth surfaces in low-pass-filtered and down-sampled low resolution (LR) to save coding bits. Because GBT does not filter across edges, it produces small or zero high-frequency components when coding smooth-surface depth maps and leads to a compact representation in the transform domain. By encoding down-sampled surface regions in LR GBT, we achieve representation compactness for a large block without the high computation complexity associated with an adaptive large-block GBT. At the decoder, encoded LR surfaces are up-sampled and interpolated while preserving encoded HR edges. Experimental results show that our proposed multi-resolution approach using GBT reduced bitrate by 68% compared to native H.264 intra with DCT encoding original HR depth maps, and by 55% compared to single-resolution GBT encoding small blocks.
Luma-Chroma Space Filter Design for Subpixel-Based Monochrome Image Downsampling

In general, subpixel-based downsampling can achieve higher apparent resolution of the down-sampled images on LCD or OLED displays than pixel-based downsampling. With the frequency domain analysis of subpixel-based downsampling, we discover special characteristics of the luma-chroma color transform choice for monochrome images. With these, we model the anti-aliasing filter design for subpixel-based monochrome image downsampling as a human visual system-based optimization problem with a two-term cost function and obtain a closed-form solution. One cost term measures the luminance distortion and the other term measures the chrominance aliasing in our chosen luma-chroma space. Simulation results suggest that the proposed method can achieve sharper down-sampled gray/font images compared with conventional pixel and subpixel-based methods, without noticeable color fringing artifacts.