Research On Images Codec Based On Compressive Sensing

The vision sensors usually sample far beyond the effective dimension of theimage signal, leading to in huge pressure the storage and transmission. CompressiveSensing (CS) provides an efficient way to acquire and reconstruct sparse signals froma limited number of linear sub-Nyquist random measurements. Such non-adaptivecompression information included in the signal samples will be collected on a smallnumber of observation data, greatly reduce the number of samples required by theaccurate reconstruction of the original signal. CS changes the image informationacquisition, transmission, and identification and gets huge success in the field ofimage processing.In traditional compressive sensing codec frame,2D images signals usually arerecast to1D vector in the observation procession which leads to lose the highdimensional structure characteristic of image data. In reconstruction procession,traditional compressive sensing codec frame usually ignores different structurecharacters of different image signal sparse bases and cannot robust recovery imagesfrom noise observation. This paper focus on these issues, proposes2D observationmethod for image signals, group sparse model for fitting sparse bases and equalizationquantization noise model for the non-uniform quantization.The main researchfocusing on the following aspects:1．For existing one-dimensional observations can not effectively get two-dimensionalinformation of images, this paper presents the method that takes2D CS observation.By studing relationship between the transform of the hybrid coding framework andCS observation, this paper presents the method that takes2D transform of imagessignal equivalent to CS observation and establishes images two dimensional tensorobserving model. This model catches different character of different dimensions ofimages, and improves efficiency the of observation process. The experimental resultsshow that this method can effectively improve the quality of the compressed sensingreconstruction.2. This paper proposes an error estimate method based on equalization andquantization noise model for image codec. Based on sparse constraint, establishes amodel between the quantization noise level and the optimization of reconstructionerror parameters. Due to the robust character of CS, it can upgrade the quality of reconstruction when error has been estimated accurately. With designed equalizationmatrix, a new norm constraint which can enhance the quality of CS recoverysignificantly has been shown. And experimental evidence exhibits more gains over CSreconstruction without error estimation.3. The wavelet transform is commonly used sparse basis in compressive sensingreconstruction. Due to wavelet transform lowest frequency coefficients take mostenergy of image while high frequency coefficients are sparse and take some importantvision information, this paper proposes a separeated grouping model based on energyfeature of wavelet coefficents; for wavelet coefficients are commonly represented bytree, this paper presents an overlapped grouping model based on wavelet zerotree.These models are complex and the corresponding algorithms based on compositesplitting and proximal method are employed to approach these group sparse models.Experimental results show that proposed models can obviously improve bothobjective and subjective qualities of image recovery.