There have been many techniques that manipulate a compressed image in the discrete cosine transform (DCT) domain for MPEG and JPEG standards, and we have proposed a model-based discontinuity evaluation technique in the DCT domain that has problems with rotated or nonideal discontinuities. To enhance the effectiveness of the discontinuity detection method in the DCT domain while preserving the advantage of efficiency, we propose a fuzzy filtering technique that consists of height fuzzification, direction fuzzification, and fuzzy filtering of discontinuities. The enhancement achieved by the fuzzy filtering includes the linking, thinning, and smoothing of discontinuities in the DCT domain. Although the detected discontinuities are rough in a low-resolution image for the size (8×8 pixels) of the DCT block, experimental results show that this technique is fast and stable to enhance the quality of discontinuities.
We propose a fuzzy method to control the bit rate in the discrete cosine transform (DCT) domain. The method consists of a bit-rate allocation with fuzzy measures and a least-distortion bit-rate reduction. Fuzzy measures are calculated from the code length, the discontinuity ambiguity, and the neighborhood momentum in each DCT block. These measures are summed with weights and form a reduction fuzziness to indicate the degree of preferable reduction. Using the reduction fuzziness, each DCT block is filtered by the least-distortion reduction method to adjust the bit rate for the target bandwidth. In an experiment, we show that the video quality transcoded by the method is better and the bandwidth is more regular than with existing methods, both visually and quantitatively.
We propose a reformed method that utilizes the motion vectors (MVs) in an MPEG sequence as the motion depicter for motion analysis and representation of video contents. The MVs are converted to a uniform MV set, independent of the frame type and the direction of prediction, and then used as motion depicters in each frame. To obtain such a uniform MV set, a new motion analysis method using bidirectional prediction-independent framework is proposed. Generally, it is impossible to directly compare an I frame without MV to others such as B or P frames. But, this approach enables a frame-type-independent representation that normalizes temporal features including frame type, macroblock (MB) encoding, and MVs. Experimental results show that our method has good performance and high validity. Compared with a full-decoding method, the average of the processing time in our method is reduced about 55%, because our method is directly processed on the MPEG bit stream after variable length code (VLC) decoding. Average of the effective number of the normalized MVs in the proposed algorithm is increased about 25% than that of the conventional method.
Because video sequences consist of dynamic video objects in nature, video object motion is an effective feature in describing the content of video sequences. In this paper, we propose a method that converts motion vectors (MVs) on MPEG coded domain to a uniform set, independent of the frame type and the direction of prediction, and directly utilizes these normalized MVs (N-MVs) in understanding video contents. To obtain such uniform MV set, we proposed a new motion analysis method using Bi-directional Prediction-Independent Framework. Generally, it is impossible to directly compare an I frame without MV to other frame types such as B or P frame. But, our approach enables a frame-type independent representation that normalizes temporal features including frame type, MB encoding and MVs. In the experiments, we show that the proposed method is better than the conventional one in terms of performance.
Video transcoding is one of the key technologies in implementing dynamic bit rate adaptation of a pre-encoded video stream to the available bandwidth over various networks. Though there have been many different transcoding schemes, since they do not concern the contents of video, the quality of transmitted video becomes proportionally worse as the rate of reduction increases. In this paper, we propose a content-based MPEG transcoding method using a discontinuity feature in the Discrete Cosine Transform (DCT) domain. The discontinuity heights in DCT blocks are evaluated by a method of model-based DCT alignment, and a DCT block is transcoded differently depending on the height of a block. In the experiment, we show the result that the video quality of content-based transcoding is better than that of a constant cut-off method and the processing time of the adaptive method is much faster compared with the pixel domain methods in the same bandwidth.
This paper presents a regularized smoothing algorithm for 3D reconstruction from image sequence. Depth data estimated from a stereo pair or multiple image frames can easily be corrupted by various types of noise such as quantization and imperfect matching. We propose a regularized image restoration algorithm which enhances the surface of depth maps based on spatially adaptive image fusion. We can also enhance the resolution of the surfaces and preserve discontinuities.
Wavelet-compressed images suffer from coding artifacts, such as ringing and blurring, resulted from the quantization of transform coefficients. In this paper we propose a new algorithm that reduces such coding artifacts in wavelet- compressed images by using regularized iterative image restoration. We, first, propose an appropriate model for the image degradation system which represents the wavelet-based image compression system. Then the model is used to formulate the regularized iterative restoration algorithm. The proposed algorithm adopts a couple of constraints, and adaptivity is imposed to the general regularization process on both spatial and frequency domain. Experimental results show that the solution of the proposed iteration converges to the image in which both ringing and blurring are significantly reduced.
DCT-based coding techniques for image data compression are widely used owing to good performance with moderate hardware complexity. In very low bit rate applications, however, block- based image compression techniques usually exhibit significant quality degradation, which is called as the blocking artifact. In this paper, we propose an adaptive fast image restoration method that is suitable for reducing the blocking artifact. The proposed restoration filter is based on an observation that the quantization operation is a nonlinear and many-to-one mapping operator. We have developed an approximated version of the constrained optimization technique for image restoration by removing the nonlinear and space-varying degradation operator. The proposed method can be used as a post-processor at the decoder of video coding systems for digital TV, video on demand (VOD), or digital versatile disc (DVD) applications.
In this paper we propose an iterative image restoration method using block edge classification for reducing block artifact in compressed images. In order to efficiently reduce block artifacts, a block is classified as edge or non-edge block, and the adaptive regularized iterative restoration method is used. The proposed restoration method is based on the observation that the quantization operation in a series of coding preprocess is a nonlinear and many-to-one mapping operator. And then we propose an adaptive iterative image restoration algorithm for removing the nonlinear and space- varying degradation. With some minor modifications the proposed image restoration method can be used for postprocessing reconstructed image sequences in HDTV, DVD, or video conferencing systems.