An interpolation oriented adaptive down-sampling algorithm is proposed for low bit-rate image coding in this paper.
Given an image, the proposed algorithm is able to obtain a low resolution image, from which a high quality image with
the same resolution as the input image can be interpolated. Different from the traditional down-sampling algorithms,
which are independent from the interpolation process, the proposed down-sampling algorithm hinges the down-sampling
to the interpolation process. Consequently, the proposed down-sampling algorithm is able to maintain the original
information of the input image to the largest extent. The down-sampled image is then fed into JPEG. A total variation
(TV) based post processing is then applied to the decompressed low resolution image. Ultimately, the processed image is
interpolated to maintain the original resolution of the input image. Experimental results verify that utilizing the downsampled
image by the proposed algorithm, an interpolated image with much higher quality can be achieved. Besides, the
proposed algorithm is able to achieve superior performance than JPEG for low bit rate image coding.
In this paper, a 3D auto-regressive (AR) model is proposed for bi-directional prediction. The prediction is composed of
two 3D AR models, which are along the forward and backward directions, respectively. Applying the 3D AR model,
each pixel in the current frame is predicted as a weighted summation of pixels within a spatial neighborhood along the
forward/backward motions within the forward/backward reference frames. Ultimately, the prediction of each pixel is
obtained as the combination of predictions generated by the two 3D AR models. To derive accurate AR coefficients, this
paper proposes a framework that performs simultaneous coefficient estimation and image interpolation. As opposed to
other methods, the predicted pixels generated by one 3D AR model are further used to predict the pixels in adjacent
frame along the motion trajectory. Consequently, each pixel in one forward/backward reference frame can be predicted
as a nonlinear combination of pixels within an enlarged spatial neighborhood along the motion in one backward/forward
reference frame. An iterative algorithm using a nonlinear least squares method is then devised to compute the optimum
3D AR coefficients. Various experiments conducted in this paper have confirmed that the proposed method has superior
performance for bi-directional prediction.
In this paper, an auto-regressive (AR) model is proposed to generate the side information for low-delay distributed video
coding (DVC). The side information generation of current Wyner-Ziv (WZ) frame <i>t</i> consists of two forward AR
interpolations. First, each pixel within the rebuilt frame <i>t</i>−1 is approximated as a linear combination of pixels within a
spatial neighborhood along the motion trajectory within the rebuilt frame <i>t</i>−2. Applying the least mean square
algorithm, the coefficient of the first forward AR model is derived. Secondly, the pixels within the rebuilt frame<i> t</i>−2
are approximated by the corresponding pixels within rebuilt frame <i>t</i>−1. And then the geometric symmetric property of
the AR model is exploited to derive the coefficient of the second forward AR model. Finally, the side information is
generated as the average of the interpolations obtained by the two forward AR interpolations. The experimental results
have demonstrated that the proposed AR model can significantly improve the PSNR of the side information compared to
existing motion extrapolation based approaches.