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High Efficiency Video Coding (HEVC) is a state-of-the-art video compression standard which improves coding efficiency significantly compared with the previous coding standard, H.264/AVC. In the HEVC standard, novel technologies consuming massive computational power are adopted, such as quad-tree-based coding unit (CU) partitioning. Although an HEVC encoder can efficiently compress various video sequences, the computational complexity of an exhaustive search has become a critical problem in HEVC encoder implementation. In this paper, we propose a fast algorithm for the CU partitioning process of the HEVC encoder using machine learning methods. A complexity measure based on the Sobel operator and rate-distortion costs are defined as features for our algorithm. A CU size can be determined early by employing Fisher’s linear discriminant analysis and the k-nearest neighbors classifier. The statistical data used for the proposed algorithm is updated by adaptive online learning phase. The experimental results show that the proposed algorithm can reduce encoding time by approximately 54.0% with a 0.68% Bjontegaard-Delta bit-rate increase.

Interleaving techniques are used to improve the probability of error correction in communication systems. In a non-cooperative context, interleaver parameters must be determined first so that the received data can be decoded into relevant information. This paper proposes blind interleaver parameter estimation method based on the Hamming weight distribution. Conventional methods based on rank distributions suffer from the high computational complexity of Gaussian elimination. In this study, we exploit the fact that the Hamming weight distributions of linear codes differ from those of random sequences owing to the linear dependence of linear codes. By exploiting this property, the proposed algorithm can estimate the interleaver period without a rank calculation. The values of the χ 2 test are used to estimate the interleaver period by constructing the Hamming weight distribution that differs the most from the binomial distribution. The experimental results indicate that the proposed algorithm outperforms conventional methods.

This paper proposes a thumbnail quality enhancement algorithm that uses a predefined weight table. Conventional thumbnail extraction algorithms in high efficiency video coding use a simple downsampling method to produce thumbnail images with low complexity, resulting in thumbnail quality deterioration. The proposed algorithm estimates the original average values of each thumbnail pixel using a weighted average value of several pixels, based on intra-mode direction. The proposed method improves the visual quality of thumbnail images while maintaining low complexity.

The latest video coding compression standard is known as highefficiency video coding (HEVC). It supports high-resolution video sequences and has better coding performance than the previous standard H.264/AVC. A quad-tree based coding unit (CU) partitioning process is one of the most efficient technologies used in an HEVC encoder. A coding tree unit (typically 6464) can be split into smaller CUs based on rate-distortion optimization, allowing various types of video content to be adaptively compressed. In addition, intra prediction of the HEVC standard supports 35 prediction modes (planar, DC, and 33 angular modes) to improve coding efficiency. However, the computational complexity of HEVC encoder becomes a critical problem when implement with an encoder. Thus, a fast CU size decision algorithm for intra prediction of an HEVC encoder is proposed in this study. We utilize image complexity and an adaptive depth prediction for early split CU decision making. In addition, the Bayesian decision rule and quadratic discriminant analysis are used for early termination of the CU partitioning process. Experimental results show that our proposed algorithm considerably reduces encoding time by approximately 55.47% with only a small BD-BR loss (1.01%) compared to the HEVC reference software HM 16.0. Predicted depth and variance difference are exploited for early-split CU detection.CUs partitioning process can be terminated by the Bayesian decision rule and quadratic discriminant analysis.Using online learning system, thresholds of proposed algorithm are periodically updated.Proposed algorithm reduces encoding time up to 55.47% with 1.01% BD-BR loss.

The wavelet domain Wiener filter has been widely adopted as an effective image denoising method that has low complexity. In this paper we propose a novel Wiener filter with high-resolution estimation that determines the signal power while preserving the edge information. We assume that a noisy image is composed of noise and the original image, which are mutually orthogonal. Based on this assumption, we utilize the local covariance to obtain high-resolution coefficients from the low-resolution coefficients and to estimate the signal variance in the Wiener filter by using the high resolution values. The experimental results show that the proposed algorithm improves the objective and subjective performance significantly.

We propose an adaptive spatial postprocessing algorithm for using in block-based discrete cosine transform (BDCT) coded images. The proposed method is comprised of three procedural steps: a thresholding step, a model classification step, and a deblocking filtering step. First, we apply adaptive thresholding to extract the pixel vector containing the blocking artifacts. This threshold has a strong correlation with image quality and the standard deviation of the pixel vector. Next, we update block types using a simple rule, thus influencing deblocking performance. Our research proposes that the activity of the pixel vector can be used to categorize the pixel vector model. With the pixel vector model determined, we are then able to apply a suitable filter to each model with different local properties. For example, we are able to utilize a directional filter to reduce ringing artifacts in edge regions. With various images and bit-rate conditions, images deblocked by the proposed method exhibit both significant visual quality improvement and PSNR gain, with fairly low computational complexity.

Demosaicking is an important step in generating a full-color image from a color filter array image captured by a single-sensor digital camera. In this paper, we propose a new demosaicking algorithm which uses Taylor series and an adaptive fusion strategy. For generating directional predictors, we present a new interpolator based on Taylor series. The directional predictors are used together with spatial and spectral correlations. Using these predictors and the adaptive fusion strategy improves the green channel interpolation, in turn helping to reconstruct the red and blue channel interpolations. Experiments comparing the proposed algorithm to some other demosaicking algorithms show that the proposed algorithm provides superior performance in terms of both objective and subjective image quality.

This paper presents a new demosaicking algorithm which uses two cost terms: the interpolation error of a low resolution image based on geometric duality and the dilated directional differentiation of color differences. Since a given high resolution image and its low resolution image obtained by sampling have similar edge properties, the proposed algorithm computes the interpolation errors for the candidate directions in the low resolution image, and exploits them as a cost term for the direction. In addition, the interpolation direction can be determined accurately even in the vicinity of object boundaries by dilating the directional differentiation of the color difference values. Through dilation, some pixels, which are in the neighborhood of an edge but classified into a flat region by simple edge detection like the Sobel filter, are reclassified. By combining this edge classifier and the weighted sum of the estimates obtained by Taylor approximation, missing pixels are interpolated. Simulation results show that the proposed demosaicking algorithm is superior to other state-of-the-art algorithms in terms of visual and objective qualities. Furthermore, the computational complexity is comparable with the existing algorithms. Therefore, the proposed algorithm is suitable for real-time implementation.

In this paper, we present a novel color image demosaicking algorithm. The algorithm consists of two steps: an interpolation step and a refinement step. The missing green color information is first interpolated by using the color channel difference. In the refinement step, a local weighted directional interpolation method guided by the pre-interpolated green channel is applied to refine the interpolation results along the determined interpolation direction. Lastly, post-processing is implemented to output the final demosaicked full color image. Due to well estimating the interpolation directions based on reliable inter-channel correlation, the interpolation accuracy is guaranteed, besides, an effective post-processing was implemented to reduce the interpolation artifacts. As a result, compared with the latest demosaicking algorithms, experiments showed that the proposed method provides superior performance in terms of both objective and subjective image qualities.

To generate a high resolution image from a low resolution one, interpolation plays a crucial role. However, conventional interpolation methods including edge-based interpolation methods have some drawbacks such as the limited number of edge directions, imprecise edge detection, and inefficient interpolation. To overcome these shortcomings, we propose a new edge-directed interpolation method, which has three aims: various edge directions, reliable edge detection, and outstanding interpolation. Since the number of candidate edge directions in the proposed method is flexible, we can use several edges included in the high resolution image. To accurately determine the edge direction, we use autocorrelation of neighboring pixels for candidate directions based on the duality between a high resolution image and its corresponding low resolution image. For the interpolation step, we utilize a Blackman–Harris windowed-sinc weighted average filter where we use correlation values obtained in the edge detection step as weights. Experimental results show that the proposed method outperforms conventional methods in terms of both the subjective and the objective results.

In this paper, we present a novel color image demosaicking algorithm based on a directional weighted interpolation method and gradient inverse-weighted filter-based refinement method. By applying a directional weighted interpolation method, the missing center pixel is interpolated, and then using the nearest neighboring pixels of the pre-interpolated pixel within the same color channel, the accuracy of interpolation is refined using a five-point gradient inverse weighted filtering method we proposed. The refined interpolated pixel values can be used to estimate the other missing pixel values successively according to the correlation inter-channels. Experimental analysis of images revealed that our proposed algorithm provided superior performance in terms of both objective and subjective image quality compared to conventional state-of-the-art demosaicking algorithms. Our implementation has very low complexity and is therefore well suited for real-time applications.

In this paper, we present a new depth upsampler in which an upsampled depth map is computed at each pixel as the average of neighboring pixels, weighted by color and depth intensity filters. The proposed method features two parameters, an adaptive smoothing parameter and a control parameter. The adaptive smoothing parameter is determined based on the ratio between a depth map and its corresponding color image. The adaptive smoothing parameter is used to control the dynamic range of the color-range filter. The control parameter assigns a larger weighting factor to pixels in the object to which a missing pixel belongs. In a comparison with five existing upsamplers, the proposed method outperforms all five in terms of both objective and subjective quality.

In this paper, a local weighted interpolation method for intra-field deinterlacing is proposed as an improved version of the DCS (deinterlacing with awareness of closeness and similarity) algorithm. The original DCS method is derived from bilateral filter which takes the local spatial closeness and pixel similarity into account when calculating the weight of interpolation. The proposed algorithm achieves three improvements: 1) instead of the line average, a more accurate interpolation filter is used to estimate the center missing pixel; 2) the center-independent interpolation method is proposed to replace the center-dependent interpolation strategy; 3) the adaptive weighted interpolation method is used to improve the accuracy of interpolation. Experimental results show that the proposed algorithm provides superior performance in terms of both objective and subjective image qualities when compared with other conventional benchmarks, including DCS algorithms with low complexity.

In this paper, we introduce an iterative second-order derivative-based intra-field deinterlacing algorithm (ISDD). The proposed ISDD is composed of two steps: (1) preprocessing by a fast second-order derivative-based deinterlacing algorithm (FSDD), (2) iteratively refining the interpolated pixel by minimizing a mass second-order function. The function includes three components: the derivative difference, directional derivative, and isophote smoothing. The derivative difference is used to detect any intensity discontinuity in an image; the directional derivative is a supplement to the derivative difference to avoid oversmoothing; and isophote smoothing can reduce artifacts. The proposed ISDD can preserve image features and reduce the artifacts caused by conventional deinterlacing methods. Compared with existing deinterlacing algorithms, the proposed algorithm improves the peak signal-to-noise ratio while maintaining high efficiency.

Current research activities in the field of deinterlacing include the selection of suitable deinterlacing methods and the estimation of the exact value of a missing line. This paper proposes a spatio-temporal domain fuzzy rough sets rule for selecting a deinterlacing method that is suitable for regions with high motion or frequent scene changes. The proposed algorithm consists of two parts. The first part is fuzzy rule-based edge-direction detection with an edge preserving part that utilizes fuzzy theory to find the most accurate edge direction and interpolates the missing pixels. Using the introduced gradients in the interpolation, the vertical resolution in the deinterlaced image is subjectively concealed. The second part of the proposed algorithm is a rough sets-assisted optimization which selects the most suitable of five different deinterlacing methods and successively builds approximations of the deinterlaced sequence. Moreover, this approach employs a size reduction of the database system, keeping only the information essential for the process. The proposed algorithm is intended not only to be fast, but also to reduce deinterlacing artifacts.

In many image sequence compression applications, Huffman coding is used to eliminate statistical redundancy resident in given data. The Huffman table is often pre-defined to reduce coding delay and table transmission overhead. Local symbol statistics, however, may be much different from the global ones manifested in the pre-defined table. In this paper, we propose three Huffman coding methods in which pre-defined codebooks are effectively manipulated according to local symbol statistics. The first proposed method dynamically modifies the symbol-codeword association without rebuilding the Huffman tree itself. The encoder and decoder maintain identical symbol-codeword association by performing the same modifications to the Huffman table, thus eliminating extra transmission overhead. The second method adaptively selects a codebook from a set of given ones, which produces the minimum number of bits. The transmission overhead in this method is the codebook selection information, which is observed to be negligible compared with the bit saving attained. Finally, we combine the two aforementioned methods to further improve compression efficiency. Experiments are carried out using five test image sequences to demonstrate the compression performance of the proposed methods.

This paper presents a new approach to adaptive Huffman coding of 2-D DCT coefficients for image sequence compression. Based on the popular motion-compensated interframe coding, the proposed method employs self-switching multiple Huffman codebooks for entropy coding of quantized transform coefficients. Unlike the existing multiple codebook approaches where the type of block (intra/inter or luminance/chrominance) selects a codebook, the proposed method jointly utilizes the type of block, the quantizer step size, and the zigzag scan position for the purpose of codebook selection. In addition, as another utilization of the quantizer step size and the scan position, the proposed method uses a variable-length “Escape” sequence for encoding rare symbols. Experimental results show that the proposed method with two codebooks provides 0.1–0.4 dB improvement over the single-codebook scheme and this margin turns out to be substantially larger than that the MPEG-2, two-codebook approach has over the single-codebook approach.