This paper deals with the problem of compressed-domain human fixations detection in the video sequences, and presents a fast and efficient algorithm based on Motion Vector Entropy (MVE) and Operational Block Description Length (OBDL). The two features are obtainable from the compressed video bitstream with partial decoding, and generate the feature maps. The two feature maps are processed, and generate MVE map and OBDL map respectively. Then the processed maps are fused. In order to further improve the global saliency detection, the fused map is worked by the Gaussian model whose center is determined by the feature values. The validation and comparison are made by several accuracy metrics on two ground truth datasets. Experimental results show that the proposed saliency detection model obtains superior performances over several state-of-the-art compressed-domain and pixel-domain algorithms on evaluation metrics. Computationally, our algorithm achieves the real-time requirements of the saliency detection.
Recently, research on and applications of human fixation detection in video compressed domain have gained increasing attention. However, prediction accuracy and computational complexity still remain a challenge. This paper addresses the problem of compressed domain fixations detection in the videos based on residual discrete cosine transform coefficients norm (RDCN) and Markov random field (MRF). RDCN feature is directly extracted from the compressed video with partial decoding and is normalized. After spatial–temporal filtering, the normalized map [Smoothed RDCN (SRDCN) map] is taken to the MRF model, and the optimal binary label map is obtained. Based on the label map and the center saliency map, saliency enhancement and nonsaliency inhibition are done for the SRDCN map, and the final SRDCN-MRF salient map is obtained. Compared with the similar models, we enhance the available energy functions and introduce an energy function that indicates the positional information of the saliency. The procedure is advantageous for improving prediction accuracy and reducing computational complexity. The validation and comparison are made by several accuracy metrics on two ground truth datasets. Experimental results show that the proposed saliency detection model achieves superior performances over several state-of-the-art compressed-domain and pixel-domain algorithms on evaluation metrics. Computationally, our algorithm reduces 26% more computational complexity with comparison to similar algorithms.
We address the problem of the lossless compression of hyperspectral images and present two efficient algorithms inspired by the distributed source coding principle, which perform the compression by means of the blocked coset coding. In order to make full use of the intraband and interband correlation, the prediction error block scheme and the multiband prediction scheme are introduced in the proposed algorithms. In the proposed algorithms, the prediction error of each 16×16 pixel block is partitioned into prediction error blocks of size 4×4. The bit rate of the pixels corresponding to the 4×4 prediction error block is determined by its maximum prediction error. This processing takes advantage of the local correlation to reduce the bit rate efficiently and brings the negligible increase of additional information. In addition to that, the proposed algorithms can be easily parallelized by having different 4×4 blocks compressed at the same time. Their performances are evaluated on AVIRIS images and compared with several existing algorithms. The experimental results on hyperspectral images show that the proposed algorithms have a competitive compression performance with existing distributed compression algorithms. Moreover, the proposed algorithms can provide low-codec complexity and high parallelism, which are suitable for onboard compression.
A lossless compression algorithm of hyperspectral image based on distributed source coding is proposed, which is used to compress the spaceborne hyperspectral data effectively. In order to make full use of the intra-frame correlation and inter-frame correlation, the prediction error block scheme are introduced. Compared with the scalar coset based distributed compression method (s-DSC) proposed by E.Magli et al., that is , the bitrate of the whole block is determined by its maximum prediction error, and the s-DSC-classify scheme proposed by Song Juan that is based on classification and coset coding, the prediction error block scheme could reduce the bitrate efficiently. Experimental results on hyperspectral images show that the proposed scheme can offer both high compression performance and low encoder complexity and decoder complexity, which is available for on-board compression of hyperspectral images.
On the basis of the theory of the biorthogonal invariant set multiwavelets (BISM) which is established by Micchelli and Xu, a biorthogonal invariant set multi-wavelets (BISM) filter is designed and the algorithms of decomposition and reconstruction of this filter are given in this paper, and it has many characteristics, such as symmetry, compact support, orthogonality and low complexity. In this filter, the self-affine triangle domain is as support interval, and constant function is as scaling function. Advantages such as low algorithm complexity, the energy and entropy in high concentration after transformation, no blocking effect to facilitate parallel computing are analyzed when the biorthogonal invariant sets multiwavelet (BISM) filters are used image compression. Finally, the validity of image compression algorithm based on biorthogonal invariant set multiwavelet is verified by the approximate JPEG2000 framework.