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Transform has been widely used to remove spatial redundancy of prediction residuals in the modern video coding standards. However, since the residual blocks exhibit diverse characteristics in a video sequence, conventional transform methods with fixed transform kernels may result in low efficiency. To tackle this problem, we propose a novel content adaptive transform framework for the H.264/AVC-based video coding. The proposed method utilizes pixel rearrangement to dynamically adjust the transform kernels to adapt to the video content. In addition, unlike the traditional adaptive transforms, the proposed method obtains the transform kernels from the reconstructed block, and hence it consumes only one logic indicator for each transform unit. Moreover, a spiral-scanning method is developed to reorder the transform coefficients for better entropy coding. Experimental results on the Key Technical Area (KTA) platform show that the proposed method can achieve an average bitrate reduction of about 7.95% and 7.0% under all-intra and low-delay configurations, respectively. [ABSTRACT FROM PUBLISHER]

In this paper, a novel reduced-reference (RR) video quality assessment (VQA) is proposed by exploiting the spatial information loss and the temporal statistical characteristics of the interframe histogram. From the spatial perspective, an energy variation descriptor (EVD) is proposed to measure the energy change of each individual encoded frame, which results from the quantization process. Besides depicting the energy change, EVD can further simulate the texture masking property of the human visual system (HVS). From the temporal perspective, the generalized Gaussian density (GGD) function is employed to capture the natural statistics of the interframe histogram distribution. The city-block distance (CBD) is used to calculate the histogram distance between the original video sequence and the encoded one. For simplicity, the difference image between adjacent frames is employed to characterize the temporal interframe relationship. By combining the spatial EVD together with the temporal CBD, an efficient RR VQA is developed. Evaluation on the subjective quality video database demonstrates that the proposed method outperforms the representative RR video quality metric and the full-reference VQAs, such as peak signal-to-noise ratio and structure similarity index in matching subjective ratings. This means that the proposed metric is more consistent with the HVS perception. Furthermore, as only a small number of RR features are extracted for representing the original video sequence (each frame requires only one parameter for describing EVD and three parameters for recording GGD), the RR features can be embedded into the video sequences or transmitted through the ancillary data channel, which can be used in the video quality monitoring system. [ABSTRACT FROM PUBLISHER]

This paper presents a directional transform scheme for coding interprediction errors in block-based hybrid video coding. It proposes a two-layer transform structure, where the first layer uses discrete wavelet transform to compact the residue energy to the LL band and then the second layer uses 2-D nonseparable directional transforms to deal with the arbitrary edge directions in the four subbands. By doing this, the edges in a macroblock are efficiently compacted to a few coefficients and at the same time the overhead used to indicate the transform directions is affordable. Experimental results show that the proposed scheme provides peak signal-to-noise ratio gain up to 0.46 dB, compared with H.264/AVC High Profile. [ABSTRACT FROM PUBLISHER]

Because of the outstanding contribution in improving compression efficiency, block-based quantization has been widely accepted in state-of-the-art image/video coding standards. However, false contour artifacts are introduced, which result in reducing the fidelity of the decoded image/video especially in terms of subjective quality. In this paper, a block-based decontouring method is proposed to reduce the false contour artifacts in the decoded image/video by automatically dithering its direct current (DC) value according to a composite model established between gradient smoothness and block-edge smoothness. Feature points on the model with the corresponding criteria in suppressing contour artifacts are compared to show a good consistency between the model and the actual processing effects. Discrete cosine transform (DCT)-based block level contour artifacts detection mechanism ensures the blocks within the texture region are not affected by the DC dithering. Both the implementation method and the algorithm complexity are analyzed to present the feasibility in integrating the proposed method into an existing video decoder on an embedded platform or system-on-chip (SoC). Experimental results demonstrate the effectiveness of the proposed method both in terms of subjective quality and processing complexity in comparison with the previous methods. [ABSTRACT FROM PUBLISHER]

In this paper, a novel reduced-reference (RR) image quality assessment (IQA) is proposed by statistical modeling of the discrete cosine transform (DCT) coefficient distributions. In order to reduce the RR data rates and further exploit the identical nature of the coefficient distributions between adjacent DCT subbands, the DCT coefficients are reorganized into a three-level coefficient tree. Subsequently, generalized Gaussian density (GGD) is employed to model the coefficient distribution of each reorganized DCT subband. The city-block distance is employed to measure the difference between the two images. Experimental results demonstrate that only a small number of RR features is sufficient for representing the image perceptual quality. The proposed method outperforms the RR WNISM and even the full-reference (FR) quality metric PSNR. [ABSTRACT FROM PUBLISHER]

Abstract: In this paper, we propose a novel Adaptive Block-size Transform (ABT) based Just-Noticeable Difference (JND) model for images/videos. Extension from 8×8 Discrete Cosine Transform (DCT) based JND model to 16×16 DCT based JND is firstly performed by considering both the spatial and temporal Human Visual System (HVS) properties. For still images or INTRA video frames, a new spatial selection strategy based on the Spatial Content Similarity (SCS) between a macroblock and its sub-blocks is proposed to determine the transform size to be employed to generate the JND map. For the INTER video frames, a temporal selection strategy based on the Motion Characteristic Similarity (MCS) between a macroblock and its sub-blocks is presented to decide the transform size for the JND. Compared with other JND models, our proposed scheme can tolerate more distortions while preserving better perceptual quality. In order to demonstrate the efficiency of the ABT-based JND in modeling the HVS properties, a simple visual quality metric is designed by considering the ABT-based JND masking properties. Evaluating on the image and video subjective databases, the proposed metric delivers a performance comparable to the state-of-the-art metrics. It confirms that the ABT-based JND consists well with the HVS. The proposed quality metric also is applied on ABT-based H.264/Advanced Video Coding (AVC) for the perceptual video coding. The experimental results demonstrate that the proposed method can deliver video sequences with higher visual quality at the same bit-rates. [Copyright &y& Elsevier]

Abstract: In this paper, a novel reduced-reference (RR) image quality assessment (IQA) is proposed by depicting the subband statistical characteristics in the reorganized discrete cosine transform (RDCT) domain. First, the block-based DCT coefficients are reorganized into a three-level coefficient tree, resulting in ten RDCT subbands. For the intra RDCT subband characteristic, the coefficient distribution of each RDCT subband is modeled by the generalized Gaussian density (GGD) function. The city-block distance (CBD) is employed to measure the modeling error between the actual distribution and the fitted GGD curve. For the inter RDCT subband characteristic, the mutual information (MI) is utilized to depict the dependencies between coefficient pairs in related RDCT subbands. Moreover, a frequency ratio descriptor (FRD) calculated in the RDCT domain is proposed to depict how the image energy distributes among different frequency components. The FRD values computed from both the reference and distorted images are jointly considered to derive a novel mutual masking strategy for simulating the texture masking property of the human visual system (HVS). By considering the GGD modeling of intra RDCT subband, MI of inter RDCT subbands, and FRD of the image, the proposed RR IQA is developed. Experimental results demonstrate that a small number of RR features is sufficient to represent the reference image for the perceptual quality analysis. The proposed method can outperform the state-of-the-art RR IQAs, and even the full-reference (FR) PSNR and SSIM. [Copyright &y& Elsevier]

In this paper, a novel reduced reference (RR) stereoscopic image quality assessment (SIQA) is proposed by characterizing the statistical properties of the stereoscopic image in the reorganized discrete cosine transform (RDCT) domain. Firstly, the difference image between the left and right view images is computed. Afterwards, the left and right view images, as well as the difference image, are decomposed by block-based discrete cosine transform (DCT). The DCT coefficients are further reorganized into a three-level coefficient tree, resulting in ten RDCT subbands. For each RDCT subband, the statistical property of the coefficient distribution is modeled by the generalized Gaussian density (GGD) function. And the mutual information (MI) and energy distribution ratio (EDR) are employed to depict the statistical properties across different RDCT subbands. Moreover, EDR can further model the mutual masking property of the human visual system (HVS). By considering the GGD modeling behavior within each RDCT subband and MI together EDR characterizing behavior across RDCT subbands, the statistical properties of the stereoscopic image are fully exploited, including the left view, right view, and the difference image. Experimental results demonstrated that the statistical properties of the difference image can well represent the perceptual quality of the stereoscopic image, which outperforms the representative RR quality metrics for stereoscopic image and even some full reference (FR) quality metrics. By considering the left view, right view, and difference image together, the performances of the proposed RR SIQA can be further improved, which presenting a more closely relationship between the quality metric output and human visual perception. [ABSTRACT FROM AUTHOR]