Video coding using geometry based block partitioning and reordering discrete cosine transform

Geometry based block partitioning （GBP） has been shown to achieve better performance than the tree structure based block partitioning （TSBP） of H.264. However, the residual blocks of GBP mode after motion compensation still present some non-vertical/non-horizontal orientations, and the conventional discrete cosine transform （DCT） may generate many high-frequency coefficients. To solve this problem, in this paper we propose a video coding approach by using GBP and reordering DCT （RDCT） techniques. In the proposed approach, GBP is first applied to partition the macroblocks. Then, before performing DCT, a reordering operation is used to adjust the pixel positions of the residual macroblocks based on the partition information. In this way, the partition information of GBP can be used to represent the reordering information of RDCT, and the bitrate can be reduced. Experimental results show that, compared to H.264/AVC, the proposed method achieves on average 6.38% and 5.69% bitrate reductions at low and high bitrates, respectively.

Reversible Data Hiding Based on Pixel-Value-Ordering and Pixel Block Merging Strategy

With the reversible data hiding method based on pixel-value-ordering,data are embedded through the modification of the maximum and minimum values of a block.A significant relationship exists between the embedding performance and the block size.Traditional pixel-value-ordering methods utilize pixel blocks with a fixed size to embed data;the smaller the pixel blocks,greater is the embedding capacity.However,it tends to result in the deterioration of the quality of the marked image.Herein,a novel reversible data hiding method is proposed by incorporating a block merging strategy into Li et al.’s pixel-value-ordering method,which realizes the dynamic control of block size by considering the image texture.First,the cover image is divided into non-overlapping 2×2 pixel blocks.Subsequently,according to their complexity,similarity and thresholds,these blocks are employed for data embedding through the pixel-value-ordering method directly or after being emerged into 2×4,4×2,or 4×4 sized blocks.Hence,smaller blocks can be used in the smooth region to create a high embedding capacity and larger blocks in the texture region to maintain a high peak signal-to-noise ratio.Experimental results prove that the proposed method is superior to the other three advanced methods.It achieves a high embedding capacity while maintaining low distortion and improves the embedding performance of the pixel-value-ordering algorithm.

A Block Mean Insertion and Dual Stego Generation Based Embedding Strategy

In reversible data hiding, pixel value ordering is an up-to-the-minute research idea in the field of data hiding. Secret messages are embedded in the maximum or the minimum value among the pixels in a block. Pixel value ordering helps identify the embeddable pixels in a block but suffers from fewer embedding payloads. It leaves many pixels in a block without implanting any bits there. The proposed scheme in this paper resolved that problem by allowing every pixel to conceive data bits. The method partitioned the image pixels in blocks of size two. In each block, it first orders these two pixels and then measures the average value. The average value is placed in the middle of these two pixels. Thus, the scheme extends the block size from two to three. After applying the embedding method of Weng et al., the implantation task removed the average value from the block to reduce its size again to two. These two alive pixels are called stego pixels, which produced a stego image. A piece of state information is produced during implanting to track whether a change is happening to the block’s cover pixels. This way, after embedding in all blocks, a binary stream of state information is produced, which has later been converted to decimal values. Thus, image data were assembled in a two-dimensional array. Considering the array as another image plane, Weng et al.’s method is again applied to embed further to produce another stego image. Model validation ensured that the proposed method performed better than previous work in this field.

A VECTOR QUANTIZATION BASED APPROACH FOR CFA DATA COMPRESSION IN WIRELESS ENDOSCOPY CAPSULE

A novel approach for near-lossless compression of Color Filtering Array (CFA) data in wireless endoscopy capsule is proposed in this paper. The compression method is based on pre-processing and vector quantization. First, the CFA raw data are low pass filtered and rearranged during pre-processing. Then, pairs of pixels are vector quantized into macros of 9 bits by applying block par-tition and index mapping in succession. These macros are entropy compressed by Joint Photographic Experts Group-Lossless Standard (JPEG-LS) finally. The complex step of codeword searching in Vector Quantization (VQ) is avoided by a predefined partition rule, which is suitable for hardware imple-mentation. By control of the pre-processor and VQ scheme, either high quality compression under un- filtered case or high ratio compression under filtered case can be realized, with the average Peak Sig-nal-to-Noise Ratio (PSNR) more than 43dB and 37dB respectively. Compared with the state-of-the-art method and the previously proposed method, our compression approach outperforms in compression performance as well as in flexibility.

A new method to retrieve aerosol optical thickness from satellite images on a parallel system

A wide variety of algorithms have been developed to monitor aerosol burden from satellite images. Still, few solutions currently allow for real-time and efficient retrieval of aerosol optical thickness （AOT）, mainly due to the extremely large volume of computation necessary for the numeric solution of atmospheric radiative transfer equations. Taking into account the efforts to exploit the SYNergy of Terra and Aqua Modis （SYNTAM, an AOT retrieval algorithm）, we present in this paper a novel method to retrieve AOT from Moderate Resolution Imaging Spectroradiometer （MODIS） satellite images, in which the strategy of block partition and collective communication was taken, thereby maximizing load balance and reducing the overhead time during inter-processor communication. Experiments were carried out to retrieve AOT at 0.44, 0.55, and 0.67μm of MODIS/Terra and MODIS/Aqua data, using the parallel SYNTAM algorithm in the IBM System Cluster 1600 deployed at China Meteorological Administration （CMA）. Results showed that parallel implementation can greatly reduce computation time, and thus ensure high parallel efficiency. AOT derived by parallel algorithm was validated against measurements from ground-based sun-photometers; in all cases, the relative error range was within 20%, which demonstrated that the parallel algorithm was suitable for applications such as air quality monitoring and climate modeling.

A novel TS-EIA-PTS PAPR reduction algorithm for optical OFDM systems

Because the partial transmit sequence(PTS) peak-to-average power ratio(PAPR) reduction technology for optical orthogonal frequency division multiplexing(O-OFDM) systems has higher computational complexity, a novel two-stage enhanced-iterative-algorithm PTS(TS-EIA-PTS) PAPR reduction algorithm with lower computational complexity is proposed in this paper. The simulation results show that the proposed TS-EIA-PTS PAPR reduction algorithm can reduce the computational complexity by 18.47% in the condition of the original signal sequence partitioned into 4 sub-blocks at the remaining stage of n-d=5. Furthermore, it has almost the same PAPR reduction performance and the same bit error rate(BER) performance as the EIA-PTS algorithm, and with the increase of the subcarrier number, the computational complexity can be further reduced. As a result, the proposed TS-EIA-PTS PAPR reduction algorithm is more suitable for the practical O-OFDM systems.