Shuyuan Zhu

Partial Video Encryption Based on Alternating Transforms

In this letter, we propose a novel video encryption technique that is used to achieve partial encryption where an annoying video can still be reconstructed even without the security key. In contrast to the existing methods where the encryption usually takes place at the entropy-coding stage or the bit-stream level, our proposed scheme embeds the encryption at the transform stage during the encoding process. To this end, we develop a number of new unitary transforms that are demonstrated to be equally efficient as the well-known DCT and thus used as alternates to DCT during the encoding process. Partial encryption is achieved through alternately applying these transforms to individual blocks according to a pre-designed secret key. Analysis on the security level of this partial encryption scheme is carried out against various common attacks and some experimental results based on H.264/AVC are presented.

MRF-Based Fast HEVC Inter CU Decision With the Variance of Absolute Differences

The newly developed High Efficiency Video Coding (HEVC) Standard has improved video coding performance significantly in comparison to its predecessors. However, more intensive computation complexity is introduced by implementing a number of new coding tools. In this paper, a fast coding unit (CU) decision based on Markov random field (MRF) is proposed for HEVC inter frames. First, it is observed that the variance of the absolute difference (VAD) is proportional with the rate-distortion (R-D) cost. The VAD based feature is designed for the CU selection. Second, the decision of CU splittings is modeled as an MRF inference problem, which can be optimized by the Graphcut algorithm. Third, a maximum a posteriori (MAP) approach based on the R-D cost is conducted to evaluate whether the unsplit CUs should be further split or not. Experimental results show that the proposed algorithm can achieve about 53% reduction of the coding time with negligible coding performance degradation, which outperforms the state-of-the-art algorithms significantly.

Perceptual Encryption of H.264 Videos: Embedding Sign-Flips Into the Integer-Based Transforms

An alternative-transforms-based scheme has recently been proposed to achieve perceptual encryption of video signals in which multiple transforms are designed by using different rotation angles at the final stage of the discrete cosine transforms (DCTs) butterfly flow-graph structure. More recently, it is found that a set of more efficient alternative transforms can be derived by introducing sign-flips at the same stage, which is equivalent to an extra rotation angle of π. In this paper, we generalize this sign-flipping technique by randomly embedding sign-flips into all stages of the DCTs butterfly structure so that the encryption space becomes much larger to yield a higher security. We pursue this study for H.264-compatible videos, assuming that the integer DCT of size 4 × 4 is used. First, we follow the separable implementation of the 4 × 4 2-D DCT in which different sign-flipping strategies will be employed along its horizontal and vertical dimensions. Second, we convert the 4 × 4 2-D DCT into a 16-point 1-D butterfly structure so that more sign-flips can be embedded at its various stages. Third, we choose different schemes to pair the node-variables in the 16-point 1-D butterfly structure, thus further enlarging the encryption space. Extensive experiments are conducted to show the performance of these improved encryption schemes and some security analyzes are also presented to confirm their persistence to various attacking strategies.

Repairing Bad Co-Segmentation Using Its Quality Evaluation and Segment Propagation

In this paper, we improve co-segmentation performance by repairing bad segments based on their quality evaluation and segment propagation. Starting from co-segmentation results of the existing co-segmentation method, we first perform co-segmentation quality evaluation to score each segment. Good segments can be filter out based on the scores. Then, a propagation method is designed to transfer good segments to the rest bad ones so as to repair the bad segmentation. In our method, the quality evaluation is implemented by the measurements of foreground consistency and segment completeness. Two propagation methods such as global propagation and local region propagation are then defined to achieve the more accurate propagation. We verify the proposed method using four state-of-the-arts co-segmentation methods and two public datasets such as ICoseg dataset and MSRC dataset. The experimental results demonstrate the effectiveness of the proposed quality evaluation method. Furthermore, the proposed method can significantly improve the performance of existing methods with larger intersection-over-union score values.

No reference image quality assessment metric via multi-domain structural information and piecewise regression

We develop a new local image representation for capturing image quality.We design a novel piecewise regression for training the quality prediction function.The proposed algorithm outperforms many representative NR-IQA methods. The general purpose no reference image quality assessment (NR-IQA) is a challenging task, which faces two hurdles: (1) it is difficult to develop one quality aware feature which works well across different types of distortion and (2) it is hard to learn a regression model to approximate a complex distribution for all training samples in the feature space. In this paper, we propose a novel NR-IQA method that addresses these problems by introducing the multi-domain structural information and piecewise regression. The main motivation of our method is based on two points. Firstly, we develop a new local image representation which extracts the structural image information from both the spatial-frequency and spatial domains. This multi-domain description could better capture human vision property. By combining our local features with a complementary global feature, the discriminative power of each single feature could be further improved. Secondly, we develop an efficient piecewise regression method to capture the local distribution of the feature space. Instead of minimizing the fitting error for all training samples, we train the specific prediction model for each query image by adaptive online learning, which focuses on approximating the distribution of the current test images k-nearest neighbor (KNN). Experimental results on three benchmark IQA databases (i.e., LIVE II, TID2008 and CSIQ) show that the proposed method outperforms many representative NR-IQA algorithms.

Perceptual video encryption using multiple 8×8 transforms in H.264 and MPEG-4

It has been demonstrated in our earlier works [1, 2] that perceptual video encryption can be effectively achieved by using multiple transforms where the block size 4×4 has been considered. In this paper, we study the extension to the transforms of size 8×8. In this case, a more complex flow-graph structure is resulted, thus leading to a larger room for encryption. In addition, special technique on controlling the encrypted video quality is presented by carefully selecting the number of rotations in the flow-graph structure of an 8×8 transform. The proposed scheme is first evaluated using the high profile of H.264. It is then further tested for the MPEG-4 standard that completely relies on 8×8 transform. Both cases show that promising results can be achieved with our proposed scheme.

Design of New Unitary Transforms for Perceptual Video Encryption

In our earlier work , we proposed for the first time that the perceptual video encryption be performed at the transformation stage by selecting one out of multiple unitary transforms according to the encryption key. In this letter, we aim to design some more efficient transforms to be used in this framework. Two criteria are followed for designing such transforms: 1) they are significantly different from discrete cosine transform (DCT) or discrete sine transform (DST), and 2) the resulted coding efficiency is exactly the same to what can be achieved by using DCT or just falls very slightly. As a result, we find that these transforms are actually derived by the sign-flipping on some node-variables in the flow-graph structure of DCT - a special case of the plane-based rotations (by π or 180°). Extensive simulations based on the H.264 codec are performed to demonstrate their effectiveness through both objective and subjective assessments. Finally, we present the security analysis to show the resistance of our algorithms to different types of attacks.

Bird breed classification and annotation using saliency based graphical model

Due to the variations among the birds, bird breed classification is still a challenging task. In this paper, we propose a saliency based graphical model (GMS), which can precisely annotate the object on the pixel level. In the proposed method, we first over-segment the image into several regions. Then, GMS extracts the object and classifies the image based on the local context, global context and saliency of each region. In order to achieve a high precision of classification, we use SVM to classify the image based on the features of the annotated bird. Finally, we employ posterior probability distribution obtained by GMS and SVM to perform the image classification. Experiments on the Caltech-UCSD Birds dataset show that the proposed model can achieve better results compared with existing bird breed classification methods based on graphical model.

Constrained Directed Graph Clustering and Segmentation Propagation for Multiple Foregrounds Cosegmentation

This paper proposes a new constrained directed graph clustering (DGC) method and segmentation propagation method for the multiple foreground cosegmentation. We solve the multiple object cosegmentation with the perspective of classification and propagation, where the classification is used to obtain the object prior of each class and the propagation is used to propagate the prior to all images. In our method, the DGC method is designed for the classification step, which adds clustering constraints in cosegmentation to prevent the clustering of the noise data. A new clustering criterion such as the strongly connected component search on the graph is introduced. Moreover, a linear time strongly connected component search algorithm is proposed for the fast clustering performance. Then, we extract the object priors from the clusters, and propagate these priors to all the images to obtain the foreground maps, which are used to achieve the final multiple objects extraction. We verify our method on both the cosegmentation and clustering tasks. The experimental results show that the proposed method can achieve larger accuracy compared with both the existing cosegmentation methods and clustering methods.

Constrained Quantization in the Transform Domain With Applications in Arbitrarily-Shaped Object Coding

In any block-based transform coding of image/video signals, it is well-known that the mean square error (MSE) distortion measured in the pixel domain is exactly equal to the MSE distortion resulted from quantization in the transform domain if the involved transform matrix is unitary. However, such a property no longer exists if the pixel-domain distortion is measured only on a selected part of pixels within one image block. This provides us an opportunity of dynamically shaping the quantization errors so as to make the selected pixels (much) better than the unselected ones. In this paper, we first develop a reversed iterative algorithm to guide us to perform a highly constrained quantization so that the coding quality of the selected pixels in each image block is significantly higher than what can be achieved by using the normal quantization. Then, we apply this intelligent quantization in one practical scenario-coding of arbitrarily-shaped image blocks in MPEG-4, showing remarkable improvements in comparison with the original MPEG-4.