nils-herrmann/clean_scirepeval huggingface dataset:
Arbitrary factor image interpolation using geodesic distance weighted 2D autoregressive modeling

Least square regression has been widely used in image interpolation. Some existing regression-based interpolation methods used ordinary least squares (OLS) to formulate cost functions. These methods usually have difficulties at object boundaries because OLS is sensitive to outliers. Weighted least squares (WLS) is then adopted to solve the outlier problem. Some weighting schemes have been proposed in the literature. In this paper we propose to use geodesic distance weighting in that geodesic distance can simultaneously measure both the spatial distance and color difference. Another contribution of this paper is that we propose an optimization scheme that can handle arbitrary factor interpolation. The idea is to separate the problem into two parts, an adaptive pixel correlation model and a convolution based image degradation model. Geodesic distance weighted 2D autoregressive model is used to model the pixel correlation which preserves local geometry. The convolution based image degradation model provides the flexibility to handle arbitrary interpolation factor. The entire problem is formulated as a WLS problem constrained by a linear equality.
Progressive adaptive correlation estimation(PACE) for WZVC

Wyner-Ziv video coding is a new paradigm for video compression, in which the prediction frames are possibly only available at the decoder. It exploits the redundancy between the source frame and the prediction frame at the decoder by utilizing their correlation information. However, such correlation information is difficult to estimate due to the absence of the prediction frame at the encoder and the lack of the source frame at the decoder. In this paper, we focus on this issue and propose a progressive adaptive correlation estimation (PACE) approach, in which the correlation information is progressively learned during the decoding process. Compared with our previous TRACE approach, PACE has similar performance in estimation accuracy as well as rate-distortion. Furthermore, it can be potentially integrated into more extensive WZVC applications, such as scalable applications and error-resilience applications.
Image colorization using sparse representation

Image colorization is the task to color a grayscale image with limited color cues. In this work, we present a novel method to perform image colorization using sparse representation. Our method first trains an over-complete dictionary in YUV color space. Then taking a grayscale image and a small subset of color pixels as inputs, our method colorizes overlapping image patches via sparse representation; it is achieved by seeking sparse representations of patches that are consistent with both the grayscale image and the color pixels. After that, we aggregate the colorized patches with weights to get an intermediate result. This process iterates until the image is properly colorized. Experimental results show that our method leads to high-quality colorizations with small number of given color pixels. To demonstrate one of the applications of the proposed method, we apply it to transfer the color of one image onto another to obtain a visually pleasing image.
Removing of blocking artefacts using error-compensation interpolation and fast adaptive spatial-varying filtering

In this paper, we propose a very effective method for removing blocking artefacts that are typically introduced by a block-based coding. The method combines an error-compensation interpolation and an adaptive spatial-varying filtering (SVF). Without destroying most key image information, the proposed method uses different filtering processes based on a simple region classification. It is observed that, compared with the coded image, this proposed scheme not only increases the resulting PSNR, but also significantly improves the visual quality. In addition, the gains (subjective and objective) that are achieved with our method are comparable to or slightly better than several other de-blocking schemes recently reported in literatures.
Fast integer motion estimation for H.264 video coding standard

Multiple block size motion estimation is adopted in the latest JVT/H.264 video coding standard to achieve higher coding efficiency. However, full exhaustive search of all block sizes is computational intensive with motion estimation complexity increasing linearly with the number of block sizes allowed. We propose a fast integer motion estimation method for multiple block size motion estimation (FMBME) which reduces computation significantly. Experimental result shows that, compared with full search, the proposed method can have a speed up factor of four with bit-rate increases within 1%.
A novel deringing method based on MAP image restoration

Long has it become a hot topic that reconstructing images of better visual quality from one or a serial of degraded ones. Although there are thousands of different restoration methods, in this paper, we focus on removing the ringing artifact caused by lossy video compression. Being a sort of restoration method, we choose the max-a-posterior(MAP) method to model this optimization problem. Quantification of the ringing artifacts serves as a prior information of the images. So it is also analyzed in this paper. The MAP optimization is further solved using a gradient decent solver. Although this is a quite classical method, there are still lots of problems with it. For settling them, we transform the solver to a filter format operation named iterative optimization filter. Experiments show that, such method could give an averaging 0.3dB gain and in special regions more than 0.7dB gain. What is more, as analyzed in this paper, the method is very preferable in terms of hardware implementation in several aspects.
Multiple Objective Frame Rate Up Conversion

In this paper, we propose a multiple objective frame rate up conversion algorithm (MOFRUC), which utilizes two different models. The first model is a constant velocity model that assumes the objects position is a linear function of time. The second model exploits the spatial correlation between neighboring blocks, and assumes the pixel intensity is highly correlated in a small local area. In this model, the perceptual quality of interpolated frame is also taken into account and the blocking artifact is minimized. Our proposed MOFRUC estimates the motion trajectory by the first model and interpolate the frame along the motion trajectory. At the same time, the algorithm refines the motion trajectory by maximizing a spatial correlation measurement defined in the second model and interpolates the frame with minimum blocking artifact. Simulation results show that our proposed MOFRUC outperforms other existing algorithms and produces high quality interpolated frame.
Secure Exp-Golomb coding using stream cipher

In this paper, we propose a secure Exp-Golomb coding scheme by incorporating with a stream cipher. Different from the traditional case of using stream cipher where the key stream is directly XORed with the plaintext, we here use the key stream to control the switching between two coding conventions (leading zeros and leading ones). Security analysis results show that the proposed system can provide high level of security with the same coding efficiency and negligible additional cost, compared with a regular Exp-Golomb coding. This scheme could potentially be applied to the state-of-the-art multimedia compression systems, e.g., H. 264, to offer security features.
Color Demosaicking Using Direction Similarity in Color Difference Spaces

This paper proposes a color demosaicking algorithm which adaptively selects direction for interpolation based on similarity of the directions in the color difference spaces. As color differences are usually smooth along the edge, the paper proposed to estimate missing channels by selecting a direction which has higher score in similarity measurement. The complexity of our proposed method is low that only addition, subtraction and shifting are required. Experimental results show that our proposed algorithm has relative high PSNR and improvement in the visual quality of reconstructed images comparing to that of state-of-art demosaicking algorithms
Multi-scale analysis of color and texture for salient object detection

In this paper we propose a multi-scale segment-based framework for salient object detection. In this framework texture and color features are used together to provide diverse information of salient object. Segmentation is performed on three different scales so that the object boundary can be accurately captured with high probability. Besides, we propose a novel adaptive feature combination mechanism to combine the saliency maps produced with different features, in which the combining weight of each saliency map is learned using online learning. Experiment results demonstrate that the proposed method significantly outperforms the state-of-the-art methods.
DCT coefficients generation model for film grain noise and its application in super-resolution

Film grain noise (FGN) is generated by the procedure of capturing pictures using photographic film. Images with FGN are subjectively pleasing. However, FGN is difficult to compress and its pleasant features are difficult to preserve when the images are resized. So in literature, FGN is extracted first, then regenerated for the processed noise-free images. In this paper a new method is proposed to generate FGN. In contrast to some other models which generate FGN in spatial domain, our method captures the statistic feature of FGN in frequency domain. FGN is signal dependent and the signal independent scaled noise image (SNI) is obtained by scaling FGN by corresponding noise-free image. We model each discrete cosine transform (DCT) coefficient of SNI as a Gaussian random variable. The Gaussian model parameters can be estimated from the stack of all the blocks in SNI based on stationary assumption. Experimental results show that proposed model recovers FGN with similar visual and spectrum properties to the original FGN. We also apply proposed model in superresolution and the quality of resultant images are improved.
Security and efficiency analysis of progressive audio scrambling in compressed domain

In this paper, we address the security and efficiency issues of two recently proposed audio scrambling schemes. We show that these two audio scrambling schemes are actually vulnerable against various attacks such as ciphertext-only attack, known-plaintext attack and chosen-plaintext attack. We also demonstrate that one of these two schemes is lack of efficiency in terms of generating the key stream using the dynamic password generator (DPG). Furthermore, we briefly discuss the ways to improve the security and efficiency of these two audio scrambling schemes.
Frame-level dependent bit allocation via geometric programming

In this paper, a novel frame-level dependent bit allocation (DBA) method is proposed. Our contribution is two-fold: First, the dependency between adjacent frames is quantitatively measured by the introduced inter-frame dependency model(IFDM). The IFDM not only holds for slow video sequences, but remains valid for video sequences of median and high motion as well. Second, based on the IFDM, the conventional DBA problem is revisited and finally it is categorized to a geometric programming problem which can be optimally and effectively solved using interior-point methods. Experimental results show that a significant gain of up to 0.5dB in PSNR can be obtained.
Image watermarking using spread spectrum technique in log-2-spatio domain

In this paper, we propose to embed the watermark information in the log-2-spatio domain by means of spread spectrum technique. In log-2-spatio domain, the variance of the information is reduced significantly. This improves the efficiency and robustness of spread spectrum technique. Low intensity and mid-band regions are selected to embed the information in order to guarantee an invisible watermark as well as the robustness to JPEG compression. Simulation results show that the embedded information still survives up to the JPEG compression ratio of 14.7.
A novel output-based objective speech quality measure for wireless communication

In our previous papers, we studied many input-to-output objective speech quality measures, some of which achieved high correlation when used to predict subjective mean opinion scores (MOS) of real cellular telephone speech samples. Two problems of input-to-output measures are that the input must be available, which is almost never the case in the cellular telephone situation, and the input must be accurately synchronized with the output. Output-based measures which do not need the input are thus highly desirable. In this paper, we propose two output-based objective speech measures which are based on visual features of the spectrogram. In our experiment, one measure OBM achieves a correlation of 0.65 which is higher than most input-to-output measures and is close to the 0.73 achieved by the best input-to-output measure.
On the Security of Multimedia Encryption Schemes Based on Multiple Huffman Table (MHT)

This paper addresses the security issues of the multimedia encryption schemes based on multiple Huffman table (MHT). A detailed analysis of known-plaintext attack is presented to show that the Huffman tables used for encryption should be carefully selected to avoid the weak keys problem. Further, we propose an efficient chosen-plaintext attack on the basic MHT method as well as the enhanced scheme inserting random bits. We also show that random rotation in partitioned bit stream cannot essentially improve the security
Improved fast motion estimation using integral projection features for hardware implementation

Conventional exhaustive search algorithm for motion estimation in video coding is optimal in mean absolute error (MAE) sense but its computation complexity is very high. Many fast algorithms such as TSS, 2DLOG and KIM were proposed to achieve large computation reduction factor at the expense of higher distortion. Previously we proposed an algorithm, IFS, to achieve considerably higher MAE than the KIM algorithm at the cost of negligible additional computational complexity. Though efficient in soft ware, IFS is not suitable for hardware implementation. In this paper, we propose a new improved algorithm, IPSS, which is more suitable for hardware implementation while achieving similar MAE as IFS.
Modified predictive motion estimation for reduced-resolution video from high-resolution compressed video

To convert a compressed video sequence to a lower-resolution compressed video, one typically needs to decompress the original sequence, down-sample each frame, and recompress it. It involves motion estimation in the reduced sequence which is computational intensive. Predictive motion estimation was proposed to generate the motion vectors of the reduced video by using the original motion information in the compressed bitstream. However, it was described only for the case of 4 to 1 down-conversion. In this paper, we propose a modified predictive motion estimation to work on other down-conversion ratios at lower complexity.
Designing an Efficient Image Encryption-Then-Compression System via Prediction Error Clustering and Random Permutation

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 image encryption and compression algorithms such that compressing the encrypted images can still be efficiently performed. In this paper, we design a highly efficient image encryption-then-compression (ETC) system, where both lossless and lossy compression are considered. The proposed image encryption scheme operated in the prediction error domain is shown to be able to provide a reasonably high level of security. We also demonstrate that an arithmetic coding-based approach can be exploited to efficiently compress the encrypted images. More notably, the proposed compression approach applied to encrypted images is only slightly worse, in terms of compression efficiency, than the state-of-the-art lossless/lossy image coders, which take original, unencrypted images as inputs. In contrast, most of the existing ETC solutions induce significant penalty on the compression efficiency.
Automatic white balancing using adjacent channels adjustment in RGB domain

Automatic white balancing is one of the key essential image pre-processing components in consumer digital still cameras, and it can highly improve the final image quality of the captured image. In this paper, a novel automatic white balancing algorithm based on the adjacent channels adjustment in the RGB domain using both luminance information and standard deviation of RGB components of the precaptured image is proposed. A light source model for evaluating different automatic white balancing methods is also described. The simulation results show that the proposed method can improve the final image quality of the captured image
Novel distortion metric for depth coding of 3D video

In state-of-the-art HEVC-based 3D video codec, multiview video plus associated depth maps are used. In order to achieve better coding performance, instead of the conventional sum of squared errors (SSE), view synthesis optimization (VSO) is proposed and included in the anchor encoder software to calculate view synthesis distortion in rate-distortion optimization (RDO) of depth coding. The anchor VSO achieves high rate-distortion (RD) performance. However, it requires partial rendering and is quite complex and time-consuming. On the other hand, simple SSE metric is fast but RD performance is low. In this paper, we propose a new distortion metric to be used in RDO for depth coding. The complexity of the proposed method is slightly higher than SSE, while its RD performance remains competitive. With a good trade-off between complexity and performance, the proposed method can replace the conventional SSE metric in RDO for depth coding, and can be used as a low-complexity alternative to the anchor VSO.
Optimized high-frequency based interpolation for multispectral demosaicking

Multispectral demosaicking, which is an extension of color demosaicking, is a challenging problem because each band is significantly undersampled and thus precise reconstruction is needed for the restoration of high-frequency components, such as edges, textures etc. In general, existing algorithms borrow high-frequency information either from different bands via inter-color correlation or from within the bands, and produces artifact in the reconstructed image. To meet this inherent shortcoming, we propose to incorporate two different high-frequency components and integrate them optimally in the linear minimum mean square sense (LMMSE) for the precise reconstruction of undersampled components. Experimental results demonstrate that the proposed algorithm based on the optimized high-frequency achieves superior performance compared to existing algorithms both in terms of objective and subjective quality.
A novel method to embed watermark in different halftone images: data hiding by conjugate error diffusion (DHCED)

In this paper, we propose a novel way called DHCED to hide invisible patterns in two or more visually different halftone images (e.g. Lena and Harbor) such that the hidden patterns would appear on the halftone images when they are overlaid. Conjugate error diffusion is used to embed the binary visual pattern in the two distinct halftone images. Simulation results show that the two halftone images have good visual quality, and the hidden pattern is visible when the two distinct halftone images are overlaid.
Sub-pixel downsampling of video with matching highly data re-use hardware architecture

Subpixel-based down-sampling is a method that can potentially improve the apparent resolution of a down-scaled image by controlling individual subpixels rather than pixels. However, the increased luminance resolution often comes at the price of chrominance distortion. A major challenge is to suppress color fringing artifacts while maintaining sharpness. In , we proposed a novel human visual quality based (HVS) subpixel downsampling method. In this paper, we propose a hardware-friendly subpixel based downsampling scheme based on our previous work which can achieve similar performance as  but eliminate all floating point operations with limited bandwidth and much better performance than Direct Pixel based Downsampling (DPD) and Pixel-based downsampling with Anti-aliasing Filter (PDAF). We further propose a hardware architecture for our subpixel downsampling method which is highly data re-useable. We are the first few, if not the first, to implement subpixel downsampling method to video by hardware. The design is implemented with TSMC 0.18um CMOS technology and costs 244k gates. At a clock frequency of 63 MHz, the architecture achieves real-time 1920×1080 subpixel downsampling at 30fps.
Advanced Real-time Rate Control in H.264

Most existing rate control schemes in the literature use one rate model and calculate quantization parameters of the macro-blocks (MB), regardless of MB types. In advanced video coding standards such as H.264, MBs belong to more advanced MB types, such as skipped and non-skipped MBs. In non-skipped MBs, the encoder determines whether each of 8times8 luminance sub-blocks and 4times4 chrominance sub-block of a MB is to be encoded, giving the different number of sub-blocks at each MB encoding times. As a result, a traditional single rate model is insufficient to represent each MB accurately. In this work, it is found that different MB types have different rate behavior. Under different conditions of MB types, we establish novel different rate models and distortion models. Our rate control scheme is proposed based on these models. The experimental results suggest that our scheme can achieve PSNR gain over JM10.2 and TMN8.
Arbitrary factor image interpolation by convolution kernel constrained 2-D autoregressive modeling

Among existing interpolation methods, convolution-based methods are able to perform arbitrary factor interpolation but the results are usually blurry or jaggy, adaptive interpolation methods usually can reduce the blurry and jaggy artifacts but cannot handle arbitrary factor interpolation. In this paper we propose an arbitrary factor adaptive interpolation algorithm by combining 2-D piecewise autoregressive (PAR) modeling and convolution kernel constraint. PAR model ensures local geometries are well preserved thus the resultant image is not blurry or jaggy. Convolution kernel constraint ensures the recovered high resolution image consistent with the low resolution image, and also provides the flexibility to handle arbitrary interpolation factor. Experiment results show that our algorithm achieves state-of-the-art performance for any interpolation factor.
Predictive motion estimation for reduced-resolution video from high-resolution compressed video

To convert a compressed video sequence to a lower-resolution compressed video, one typically needs to decompress the original sequence, down-sample each frame, and recompress it. It involves motion estimation in the reduced sequence which is computational intensive. We propose a novel fast algorithm to predict the motion vector of the reduced video by using the original motion information in the compressed bitstream. We achieve a much higher quality than existing algorithms with low additional complexity.