A novel statistical image model is proposed to facilitate the design and analysis of image processing algorithms. A mean-removed image neighborhood is modeled as a scaled segment of a hypothetical texture source, characterized as a 2-D stationary zero-mean unit-variance random field, specified by its autocorrelation function. Assuming that statistically similar image neighborhoods are derived from the same texture source, a clustering algorithm is developed to optimize both the texture sources and the cluster of neighborhoods associated with each texture source. Additionally, a novel parameterization of the texture source autocorrelation function and the corresponding power spectral density is incorporated into the clustering algorithm. The parametric auto-correlation function is anisotropic, suitable for describing directional features such as edges and lines in images.
Experimental results demonstrate the application of the proposed model for designing linear predictors and analyzing the performance of wavelet-based image coding methods.
The direction-adaptive discrete wavelet transform (DA-DWT) locally adapts the filtering direction to the geometric
flow in the image. DA-DWT image coders have been shown to achieve a rate-distortion performance superior
to non-adaptive wavelet coders. However, since the direction information must always be signalled regardless of
total bit-rate, performance at very low bit-rates might be worse. In this paper, we propose two scalable direction
representations: the layered scheme which is similar to the scalable motion vector representation in scalable
video coding and the level-unit scheme which provides finer granularity upon the layered scheme. Experimental
results indicate that we can achieve the desirable performance at both low and high bit rates with our proposed
level-unit scheme. Significant improvement in image quality (about 3-5 dB) is observed at very low bit rate,
relative to non-scalable coding of the direction information.
A rate-distortion optimized scheme for interactive light field streaming is proposed. The light field data set is transformed into blocks of wavelet coefficients; each block is coded as a
scalable bitstream and stored at the sender. To render a frame, the receiver issues a request for relevant data. Based on the request, the estimated state of the data already at the receiver, the network characteristics, and the desired transmission rate, the sender customizes the outgoing packets in order to minimize the distortion experienced at receiver. Experimental results show that the proposed rate-distortion optimized scheme improves the rendering quality by 0.5~2.1 dB in PSNR over a heuristic scheme at the same rate. Alternatively, it reduces the required bit-rate by 10%~25% over the heuristic scheme at the same rendering quality.
We propose a novel approach that uses disparity-compensated
lifting for wavelet compression of light fields. Disparity
compensation is incorporated into the lifting structure for the
transform across the views to solve the irreversibility limitation
in previous wavelet coding schemes. With this approach, we obtain
the benefits of wavelet coding, such as scalability in all
dimensions, as well as superior compression performance. For light
fields of an object, shape adaptation is adopted to improve the
compression efficiency and visual quality of reconstructed images.
In this work we extend the scheme to handle light fields with
arbitrary camera arrangements. A view-sequencing algorithm is
developed to encode the images. Experimental results show that
the proposed scheme outperforms existing light field compression
techniques in terms of compression efficiency and visual quality
of the reconstructed views.