nils-herrmann/clean_scirepeval huggingface dataset:
Automatic 2D-to-3D video conversion technique based on depth-from-motion and color segmentation

Most of the TV manufacturers have released 3DTVs in the summer of 2010 using shutter-glasses technology. 3D video applications are becoming popular in our daily life, especially at home entertainment. Although more and more 3D movies are being made, 3D video contents are still not rich enough to satisfy the future 3D video market. There is a rising demand on new techniques for automatically converting 2D video content to stereoscopic 3D video displays. In this paper, an automatic monoscopic video to stereoscopic 3D video conversion scheme is presented using block-based depth from motion estimation and color segmentation for depth map enhancement. The color based region segmentation provides good region boundary information, which is used to fuse with block-based depth map for eliminating the staircase effect and assigning good depth value in each segmented region. The experimental results show that this scheme can achieve relatively high quality 3D stereoscopic video output.
Hierarchical image vector quantizer using gradient-Laplacian subspace distortion

A new hierarchical mean-residual image vector quantization (HMRIVQ) technique using a general four-dimensional gradient-Laplacian subspace distortion (GLSD) is proposed. The GLSD is used for design and implementation of different block sizes of the HMRIVQ. Therefore, the computational and storage costs of the encoder are significantly reduced. The HMRIVQ can also achieve a very high compression rate for low detailed images. A performance comparison of the HMRIVQ using Euclidean distortion and the general four-dimensional GLSD is also given.<<ETX>>
Directionally classified subspace image vector quantization

Describes a new image coding scheme called directionally classified subspace vector quantization which is based on the dimensionality reduced subspace distortion measurement technique and the classified vector quantization technique for reducing the computational complexity and memory requirement of the image vector quantizer. In the new coding scheme, the 4*4 image block is classified into one of nine classes according to the directional content of the image block which is vector quantized using appropriate Hadamard transform subspace distortion measure. The classification is based on the horizontal and vertical gradients of the image block. The two gradient parameters form a 2-dimensional space which can be partitioned into 9 regions and each region correspond to a class of vectors. As the subspace vector quantization is applied on the restricted class of vector, extremely low dimensionality subspace distortion measures can be used. Thus, the computational complexity and memory requirement of the coder are both significantly reduced, while the reconstructed image quality is preserved for edges.<<ETX>>
Adjustable partial distortion search algorithm for fast block motion estimation

The quality control for video coding usually absents from many traditional fast block motion estimators. A novel block-matching algorithm for fast motion estimation named the adjustable partial distortion search algorithm (APDS) is proposed. It is a new normalized partial distortion comparison method capable of adjusting the prediction accuracy against searching speed by a quality factor k. With adjustability, APDS could act as the normalized partial distortion search algorithm (NPDS) when k is equal to 0, and the conventional partial distortion search algorithm (PDS) when k is equal to 1. In addition, it uses a halfway-stop technique with progressive partial distortions (PPD) to increase early rejection rate of impossible candidate motion vectors at very early stages. Simulations with PPD reduce computations up to 38 times with less than 0.50-dB degradation in PSNR performance, as compared to the full-search algorithm (FS). Experimental results show that APDS could provide peak signal-to-noise ratio performance very close to that of FS with speedup ratios of 7 to 16 times, and close to that of NPDS from 22 to 32 times, respectively, as compared to FS.
A Novel Computerized Method Based on Support Vector Machine for Tongue Diagnosis

The tongue diagnosis is an important diagnostic method in traditional chinese medicine (TCM). In this paper, we present a novel computerized tongue inspection method based on support vector machine (SVM). First, two kinds of quantitative features, chromatic and textural measures, are extracted from tongue images by using popular image processing techniques. Then, support vector machine and Bayesian network are employed to build the mapping relationships between these features and diseases, respectively. Finally, we present a comparison between SVM and BN classification. The experiment results show that we can use SVM to classify the tongue images more excellently and get a relative reliable prediction of diseases based on these features.
Multi-direction search algorithm for block-based motion estimation

Easily trapped in local minima is one of the well-known problems in search point pattern based fast block motion estimation algorithms. This problem is especially serious in one-at-a-time search (OTS) and block-based gradient descent search (BBGDS). These two algorithms can provide very high speedup ratio but with low robustness in prediction accuracy especially for sequences with complex motions. Multi-path search (MPS) using more than one path have been proposed to improve the robustness of BBGDS, but the computational requirement is much increased. To tackle this problem, a novel multi-directional gradient descent search (MDGDS) is proposed in this paper with use of multiple OTSs in eight directions. Basically, the proposed MDGDS performs eight one-dimensional gradient descent searches on the error surface and therefore can trace to the global minimum more efficiently. Experimental results show that a significant improvement in computation reduction can be achieved as compared with well-known fast block motion estimation algorithms.
Hybrid search algorithm for block motion estimation

A hybrid block matching algorithm for motion estimation is proposed in this paper. In this algorithm, a motion block is classified as either large or small motion by making use of the correlation between the block distortion measure of the origin checking point and the magnitude of the motion vector. According to the classified motion type, one of the two fast block matching algorithms, the four-step search and block-based gradient descent search, is employed for the motion estimation. An adaptively updated threshold is used for the motion type classification. Experimental results show that the proposed algorithm can achieve better MSE performance than the two algorithms due to the increased chance for the individual algorithm to search its favorite motion type.
No-reference image quality assessment with deep convolutional neural networks

The state-of-the-art general-purpose no-reference image or video quality assessment (NR-I/VQA) algorithms usually rely on elaborated hand-crafted features which capture the Natural Scene Statistics (NSS) properties. However, designing these features is usually not an easy problem. In this paper, we describe a novel general-purpose NR-IQA framework which is based on deep Convolutional Neural Networks (CNN). Directly taking a raw image as input and outputting the image quality score, this new framework integrates the feature learning and regression into one optimization process, which provides an end-to-end solution to the NR-IQA problem and frees us from designing hand-crafted features. This approach achieves excellent performance on the LIVE dataset and is very competitive with other state-of-the-art NR-IQA algorithms.
A new prediction model search algorithm for fast block motion estimation

Most of the fast block matching algorithms (BMAs) use the origin as the initial center of the search. To improve the accuracy of the fast BMAs, a new prediction model (PM) search algorithm is proposed. Based on the spatial correlation of motion and the fast center-biased BMA, the four causal neighbour blocks are chosen to predict the starting search point and then the center-biased BMA is used to find the final motion vector as the global minimum is closer to the predicted center. Experimental results show that the proposed prediction model improves the performance of most fast BMAs such as the new three-step search and the four-step search, in terms of the motion compensation errors. In addition, the average computational requirement is also reduced.
A New Partial Codeword Updating Scheme Based on Rate-Distortion Optimization for Adaptive Vector Quantization

In this paper, we propose a new adaptive vector quantization (AVQ) algorithm based on the rate-distortion optimization. This algorithm employs a new partial codeword updating (PCU) scheme which achieves rate-distortion performance superior to that of the conventional AVQ algorithms using the full codeword updating (FCU) scheme. The PCU-AVQ only updates the codeword's components with the quantization error higher than an optimal threshold instead of replacing the whole codeword. Additionally, the mathematical relation between the Lagrangian multiplier and the approximate optimal threshold is devised to reduce the rate-distortion cost computation. The experimental results show that the proposed PCU-AVQ algorithm indeed improves the rate-distortion performance without much computational complexity penalty. The PCU-AVQ can be combined with transform coding and entropy coding for higher compression ratio, and it can be widely implemented in specific AVQ algorithms for image, video and speech coding.
Dynamic candidate keypad for stroke-based Chinese input method on touchscreen devices

With the popularity of touchscreen devices, physical button-based keyboards are being replaced by finger-operated virtual keyboards. Entering text on these touchscreen devices is no longer limited by finger tapping activities on the keys. Text input can be performed by finger sliding over the virtual keyboard, which is well realized by the Swype technology on a typical QWERTY keyboard. Such shorthand gesturing for text input, however, may be inefficient when directly applied to Chinese input, especially in Chinese stroke-based input method. In this paper, a novel dynamic candidate keypad with use of unidirectional finger gesture on the stroke key for character selection is proposed for enhancing stroke-based Chinese input method. The new design can enhance frequently used Chinese characters searching and input using stroke-based input. The new method is implemented on the Android 2.2 platform for performance evaluation using Traditional Chinese characters set. Experimental results show that the proposed method enables users to input popular Chinese characters easier than conventional stroke-based input methods.
Novel Directional Gradient Descent Searches for Fast Block Motion Estimation

Search point pattern-based fast block motion estimation algorithms provide significant speedup for motion estimation but usually suffer from being easily trapped in local minima. This may lead to low robustness in prediction accuracy particularly for video sequences with complex motions. This problem is especially serious in one-at-a-time search (OTS) and block-based gradient descent search (BBGDS), which provide very high speedup ratio. A multipath search using more than one search path has been proposed to improve the robustness of BBGDS but the computational requirement is much increased. To tackle this drawback, a novel directional gradient descent search (DGDS) algorithm using multiple OTSs and gradient descent searches on the error surface in eight directions is proposed in this letter. The search point patterns in each stage depend on the minima found in these eight directions, and thus the global minimum can be traced more efficiently. In addition, a fast version of the DGDS (FDGDS) algorithm is also described to further improve the speed of DGDS. Experimental results show that DGDS reduces computation load significantly compared with the well-known fast block motion estimation algorithms. Moreover, FDGDS can achieve faster speedup compared with the UMHexagonS algorithm in H.264/AVC implementation while maintaining very similar rate-distortion performance.
Search Patterns Switching for Motion Estimation using Rate of Error Descent

Most of the fast motion estimation algorithms based on search-point pattern are only good at handling videos with small motions, for example, block-based gradient descent search, diamond search and hexagonal-based search. An adaptive motion estimation algorithm which can switch between search patterns for different video contents should work better than a single search pattern algorithm. In this paper, a simple classifier based on error descent rate (EDR) is proposed. This classifier uses a very few number of search points to predict whether the global minimum is far away or near the center of the search window. If it is far away, a search pattern which is good at searching large motions is used. Otherwise, a pattern good at searching small motions is applied. The proposed search patterns switching (SPS) algorithm performs well for all kinds of video contents.
MIRROR: an interactive content based image retrieval system

A content based image retrieval system, called MPEG-7 image retrieval refinement based on relevance feedback (MIRROR), is developed for evaluating MPEG-7 visual descriptors and developing new retrieval algorithms. The system core is based on MPEG-7 experimentation mode (XM) with Web-based user interface for query by image example retrieval. A new merged color palette approach for MPEG-7 dominant color descriptor similarity measure and relevance feedback are also developed in this system. Several MPEG-7 visual descriptors are adopted in MIRROR for performance comparison purposes.
Evaluation of Perceptual Resolution and Crosstalk in Stereoscopic Displays

Perceptual resolution and crosstalk of active and passive stereoscopic 3D displays are evaluated by subjective tests. Test patterns of gratings with various contrast and spatial frequencies are used to determine the contrast thresholds, from which the contrast sensitivity of human visual systems is obtained. Perceptual resolution tests show that viewers can perceive the highest vertical frequency on active displays, but not on passive displays. However, the contrast sensitivity is higher for passive displays than active displays for all other frequencies. Viewers can perceive frequency components with lower contrast, except for the highest frequency, more easily on passive displays than on active displays. Perceptual crosstalk tests show that interocular crosstalk is a function of spatial frequency. Crosstalk is lower on passive displays than on active displays.
Judder-free reverse pull-down using dynamic compensation

This paper proposes a new judder-free reverse pull-down algorithm for film images. Conventional reverse pull-down methods have troubles in dealing with TV screen conversion of film images that have been edited in wrong ways, in which processed images often suffer from frame-wise or object-wise judder. The proposed judder-free pull-down method is based on dynamic compensation, which allows us to correctly cope with images containing bad-edition. In addition, it performs judder detection, which prevents judder from degrading interlaced computer generated imagery (CGI) on film images.
A wavelet packet-based noise reduction algorithm of NTSC images using CVBS characteristics

This paper proposes a wavelet packet-based noise reduction algorithm for national television system committee (NTSC) images, in which the characteristics of a composite video burst signal (CVBS) are utilized. Most of conventional noise reduction algorithms apply spatial or spatio-temporal filtering only to the luminance signal, noting that the human eye is less sensitive to color than to luminance. Such noise reduction algorithms do not consider the realworld situation where a TV signal is transmitted over a noisy channel and decoded at a receiver. In this paper, it is assumed that an NTSVC signal is transmitted as a CVBS and corrupted with white Gaussian noise (WGN) by the channel. A CVBS has characteristics different from those of speech or image signals in a sense that encoded color information is modulated onto a high-frequency color subcarrier. The wavelet packet-based approach is suitable for noise reduction of the CVBS because decomposing a one-dimensional CVBS into eight subbands provides a chance to process each subband separately. In the proposed wavelet packet-based noise reduction algorithm, wavelet packet filtering is employed in subbands containing the color information whereas Wiener filtering is used in the other subbands. The separate treatment of each wavelet subband depending on the characteristics of CVBS leads to effective color and edge preserving noise reduction. The performance of the proposed method is validated by experiments with generated and corrupted CVBS images. Experimental results with various test images show that the proposed algorithm is effective in terms of the noise reduction efficiency and edge and color preservation.
Block-based noise estimation using adaptive Gaussian filtering

We propose a fast noise estimation algorithm using a Gaussian pre-filter. The coefficients of the Gaussian filter are selected according to the standard deviation of the Gaussian noise estimated from an input noisy image. The standard deviation is estimated from the difference image between the noisy input image and its filtered image. This algorithm can be applied to noise reduction in commercial image- or video-based applications such as digital cameras and digital television (DTV) for its performance and simplicity.
An effective de-interlacing technique using two types of motion information

We propose a new de-interlacing algorithm using two types of motion information, i.e., the block-based and the pixel-based motion information. In the proposed scheme, block-wise motion is first calculated using the frame differences. Then, it is refined by the pixel-based motion information. The results of hardware implementation show that the proposed scheme using block-wise motion is more robust to noise than the conventional schemes using pixel-wise motion. Also, the proposed spatial interpolation provides a good visual performance in the case of moving diagonal edges.
Superresolution of a Region of Interest Using Feature-Based Affine Motion Estimation

This paper presents a low-computational superresolution method of a region of interest (ROI) using multiple low-resolution input images of the same scene. We extract the regions from the multiple input images that are similar to the selected ROI in the reference image and use feature points to estimate the affine motion parameters. We apply a projection onto convex sets based method to interpolate the ROI using the estimated motion and simplify the iterative computation of the whole system, in which an edge-preserving smoothing filter is utilized to reduce the motion compensation error caused by additive noise. Experiments with several test image sets show the effectiveness of the proposed method.
Environmental illumination invariant face recognition using near infrared imaging system

Environmental illumination can degrade performance of face recognition systems. This paper presents a method to reduce the effect of environmental illumination for face recognition with near infrared (NIR) imaging systems. The camera response function that maps the image irradiance to image brightness is estimated via principal component analysis with pairs of facial images taken under different lighting conditions. The inverse of camera response function is used to map two facial images of a subject taken with and without frontal NIR lighting under ambient light. The irradiance of facial image with frontal NIR lighting in the absence of ambient light is estimated using the inverse camera response function. Then the brightness image with only the frontal NIR lighting is obtained from the irradiance to be used for face recognition. Since the brightness images are used by face recognition system when there is no ambient light, the same face recognition algorithm and database can be used without any modification. The proposed method is a pre-processing method and does not require additional hardware. Performance is evaluated and compared to existing methods at various false matching rates. The proposed method can enhance the performance of face recognition in the presence of ambient light at false matching rates at which practical biometric authentication systems operate.
Pattern matching assisted motion estimation and motion vector histogram analysis for interlaced-to-progressive conversion

Motion compensated interlaced-to-progressive conversion (MC-IPC) methods employ implicit decision on whether to use the estimated motion vectors in order to prevent spurious errors due to poorly estimated motion vectors. This often limits the performance of the MC-IPC. We present an IPC method based on the pattern matching assisted motion estimation and motion vector histogram analysis. The proposed method employs an explicit decision on whether to use the estimated motion vectors. Extensive experiments show that high quality IPC can be achieved without spurious errors.
Efficient nonlinear geometric distortion correction for scanned laser pico projectors

Scanned laser pico projectors utilising laser light sources and microelectromechanical systems mirror devices introduce geometric distortion to the displayed images because of nonlinear mirror driving methods. A pre-processing algorithm is proposed that corrects the nonlinear geometric distortion of scanned laser pico projectors. The distortion is modelled as blockwise homography transforms. The geometry of the images being displayed is pre-distorted using the pixel indices obtained by the backward blockwise homography transforms. The pre-distorted images are displayed by the pico projector whose geometrical distortion undoes the pre-distortion such that distortion-free images can be displayed. The proposed algorithm requires a small amount of information stored in the memory and three bilinear interpolations per pixel, thus providing an efficient way to correct nonlinear distortion without additional optical components, such as aspherical lenses.
Adaptive motion-compensated frame rate up-conversion

A frame rate up-conversion algorithm using the adaptive motion compensation that reduces the blocking artifacts due to block-based processing is proposed. The proposed method uses the motion analysis to determine the type of motion and applies the motion-compensated interpolation (MCI) adaptively. When the MCI is applied, multiple motion trajectories are considered in order to increase the accuracy of the MCI. Experiment results indicate good performance of the proposed MCI scheme with significantly reduced blocking artifacts.
Coding artifacts removal using biased anisotropic diffusion

Biased anisotropic diffusion is applied to the coding artifacts removal of the DCT based codec. It is formulated as a cost minimization problem. The weighting factors of the cost function are controlled such that the solution removes the blocking effect and conceals the block losses. It has an advantage over other postprocessing schemes because it handles the discontinuity of the image, smoothes the image selectively, and takes the visual masking in to account. Features needed for the weighting factors are extracted directly from the DCT coefficients to reduce the computational complexity.