Researches On Low-complexity Compression Algorithm Of Image And Video

As the image and video data is information carrier closely related to human senses,they have become an indispensable part of the production and living.With the rapid development of network communication,large scale integrated circuit,sensor technology and so on,and the rapid promotion of people's needs such as living,entertainment,learning and scientific research,not only the way of getting image data is increasing but also spectral resolution,spatial resolution,temporal resolution and the depth of the quantization improve constantly.This leads to the amount of image data increase exponentially.Whether from the speed of equipment renewal or economic conditions,only the hardware support can no longer meet the actual needs,and one effective solution to reduce the amount of data,save storage space and transmission bandwidth is the image data compression coding.The image data compression coding technology seeks to use the least data to express the source image signal.It is an important and necessary technology in fields of communication,broadcasting,storage,multimedia entertainment and so on.The image data compression coding technology is becoming mature and standardized.It make that massive compressed image data are widely existed.How to extract directly the information which people need from these massive compressed data for further services such as compression and transcoding,becomes a new international research hotspot.This eliminates the complex process of decompressing/recompressing,and avoid double calculating the information which exists in the compressed domain,which greatly improves the integrity and real-time of the data application system.As the front-end compressor has largely eliminated the redundancy of images and video,it is difficult to meet the actual requirements in the re-encoding process if the recompression method is still used for the whole image.Therefore,based on the significance perception of the video image compression domain recompression system has gradually received people's attention,and in the system compression domain significant detection is the key.In this paper we focus on the research of image and video data compression technology,including image data compression algorithm and the automatic extraction of the region of interest.The main contributions and innovation points of the dissertation are taken as follows:1.Due to the high complexity and energy dispersion of the wavelet transform in JPEG2000,here we propose image compression algorithm based on the biorthogonal invariant set multiwavelet.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.2.To solve the problem of lossless compression algorithm for hyperspectral images,here we 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 intra-band and inter-band 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.3.To solve the problem of low detection accuracy and high computational complexity of human fixations prediction in video compressed-domain.This paper addresses the problem of compressed-domain video human fixations prediction based on saliency detection,and presents a fast and efficient algorithm based on Residual DCT Coefficients Norm(RDCN feature)and Operational Block Description Length(OBDL feature).These two features are directly extracted from the compressed bit-stream with partial decoding,and are normalized.After spatial and temporal filtering,the normalized salient maps are fused by the dynamic fusion coefficients with variation of quantization parameters.Then the fused salient map is worked by Gaussian model whose center is determined by the feature values.The proposed saliency detection model for human fixations prediction combines the accuracy of the pixel-domain saliency detections with the computational efficiency of their compressed-domain counterparts.The validation and comparison are made by several accuracy metrics on two ground truth datasets.Experimental results show that the proposed saliency detection model for human fixations prediction obtains superior performances over several state-of-the-art compressed-domain and pixel-domain algorithms on evaluation metrics.Computationally,our algorithm achieves a speed-up of over 10 times as compared to similar algorithms,which illustrates it appropriate for in-camera saliency estimation.4.Due to the locality of human fixations prediction in video compressed-domain,this paper addresses the problem of compressed-domain fixations detection in the videos based on Residual DCT Coefficients Norm 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(SRDCN map)is taken to MRF model,and the optimal binary label map is obtained.Based on the label map and the center saliency map,saliency enhancement and non-saliency 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 novel energy function which indicates the positional information of the saliency.The procedure is advantageous to improve prediction accuracy and reduce 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.