Hsueh-Ming Hang

Most previous studies on visual saliency focused on two-dimensional (2D) scenes. Due to the rapidly growing three-dimensional (3D) video applications, it is very desirable to know how depth information affects human visual attention. In this study, we first conducted eye-fixation experiments on 3D images. Our fixation data set comprises 475 3D images and 16 subjects. We used a Tobii TX300 eye tracker (Tobii, Stockholm, Sweden) to track the eye movement of each subject. In addition, this database contains 475 computed depth maps. Due to the scarcity of public-domain 3D fixation data, this data set should be useful to the 3D visual attention research community. Then, a learning-based visual attention model was designed to predict human attention. In addition to the popular 2D features, we included the depth map and its derived features. The results indicate that the extra depth information can enhance the saliency estimation accuracy specifically for close-up objects hidden in a complex-texture background. In addition, we examined the effectiveness of various low-, mid-, and high-level features on saliency prediction. Compared with both 2D and 3D state-of-the-art saliency estimation models, our methods show better performance on the 3D test images. The eye-tracking database and the MATLAB source codes for the proposed saliency model and evaluation methods are available on our website.

Pattern-based block motion estimation (PBME) is one of the most widely adopted compression tools in the contemporary video coding systems. However, despite that many researches have studied PBME, few have yet attempted to construct an analytical model that can explain the underneath principle and mechanism of various PBME algorithms. In this paper, we propose a statistical PBME model that consists of two components: 1) a statistical probability distribution for motion vectors and 2) the minimal number of search points (so-called weighting function) achieved by a search algorithm. We first verify the accuracy of the proposed model by checking the experimental data. Then, an application example using this model is shown. Starting from an ideal weighting function, we devise a novel genetic rhombus pattern search (GRPS) to match the design target. Simulations show that, comparing to the other popular search algorithms, GRPS reduces the average search points for more than 20% and, in the meanwhile, it maintains a similar level of coded image quality.

Pattern-based block motion estimation (PBME) algorithm has been widely adopted in digital video coding systems. Due to the large characteristics variations among video sequences, adaptive PBME algorithms that switch search patterns have been proposed. However, most adaptive search algorithms are heuristically designed based on the experimental data. In this paper, we like to construct an analytical model and explore the problem systematically. Also, we propose an adaptive genetic pattern search algorithm (AGPS). Simulations show that the proposed AGPS in average outperforms the existing popular search algorithms quite significantly in speed, while the peak signal noise ratio (PSNR) quality is maintained at the same level.

JPEG2000 provides excellent compression performance and fine granularity scalability but at the cost of high computational complexity. We propose two speed-up techniques and use the TI DSP optimization tools to accelerate the Tierl module. We eliminate the unnecessary checking cycles by recording the NBC (need-to-be-coded) samples on a list. Furthermore, the sample index is reordered to facilitate fast execution. In the DSP implementation of the proposed methods, we use code acceleration techniques, cache memory allocation, and TI DSP compiler-level optimization tools. Even when the original program is compiled with the same DSP optimization tools and proper cache assignment, our fast algorithm can still reduce the computation by 45%.

Content-based image search has long been considered a difficult task. Making correct conjectures on the user intention (perception) based on the query images is a critical step in the content-based search. One key concept in this paper is how we find the user preferred low-level image characteristics from the multiple positive samples provided by the user. The second key concept is how we generate a set of consistent "pseudo images" when the user does not provide a sufficient number of samples. The notion of image feature stability is thus introduced. The third key concept is how we use negative images as pruning criterion. In realizing the preceding concepts, an image search scheme is developed using the weighted low-level image features. At the end, quantitative simulation results are used to show the effectiveness of these concepts.

In this paper, a new bit allocation algorithm, called EBFOS (enhanced BFOS), is proposed for the MPEG-4 advanced audio coding (AAC) scheme. The notion of "bit-use efficiency" is suggested and the basic idea behind our approach is "give bits to the band with the maximum NMR gain per bit" or "retrieve bits from the band with the maximum bits per NMR loss", which is similar to the basic concept of the generalized BFOS (Breiman, Friedman, Olshen, and Stone) algorithm. However, the inter-band dependency of coding process in AAC is also taken into consideration in our approach. Simulation results show that the performance of the EBFOS scheme is significantly better than that of the MPEG-4 AAC verification model and is close to that of the trellis-based scheme, which achieves the minimum of a predefined NMR cost function. Also proposed in this paper is a fast version of our algorithm to reduce its computational complexity.