This paper proposes a multiresolution method for video object segmentation in the compression domain. We first calculate global motion parameters using only background macroblocks with tiny residual dc coefficients of the P frame, and then get true motion vectors projected to the immediate adjoined I frame. The basic layer image is obtained with only dc coefficients of the I frame. The enhancement texture characteristics are provided by the ac coefficients for partial decoding. The true object motion vectors and the basic layer image are fed into a morphological motion filter to get the lowest-resolution regions of moving objects, called the layer4 region of interest (L4-ROI). Only some of the ac coefficients in L4-ROI are decoded to obtain a higher-resolution image, called layer3, that mainly consists of blocks of the moving object. The moving object of interest in the highest resolution is obtained from a morphological motion filter with L2-ROI and the true object motion vectors. The number of ac coefficients determines the resulting resolution. Experiments show that the new algorithm can extract multiresolution moving objects efficiently.
Since the current rate control schemes in H.264 do not present the capability of efficient frame-level bit allocation, the video quality varies greatly for sequences with scene cuts or large motions. To overcome that limitation, we propose a rate control scheme based on the incremental proportional-integral-differential (PID) algorithm. That algorithm is introduced to control the buffer and decrease the influence of buffer surface fluctuation on the process of frame-level bit allocation. Extensive simulation results show that this rate control scheme, without expensive computational complexity added, decreases the average video quality variations by 21.07%.
Moving object retrieval technique in compressed domain plays an important role in many real-time applications, e.g. Vehicle Detection and Classification. A number of retrieval techniques that operate in compressed domain have been reported in the literature. H.264/AVC is the up-to-date video-coding standard that is likely to lead to the proliferation of retrieval techniques in the compressed domain. Up to now, few literatures on H.264/AVC compressed video have been reported. Compared with the MPEG standard, H.264/AVC employs several new coding block types and different entropy coding method, which result in moving object retrieval in H.264/ AVC compressed video a new task and challenging work. In this paper, an approach to extract and retrieval moving traffic object in H.264/AVC compressed video is proposed. Our algorithm first Interpolates the sparse motion vector of p-frame that is composed of 4*4 blocks,
4*8 blocks and 8*4 blocks and so on. After forward projecting each p-frame vector to the immediate adjacent I-frame and calculating the DCT coefficients of I-frame using information of spatial intra-prediction, the method extracts moving VOPs (video object plan) using an interactive 4*4 block classification process. In Vehicle Detection application, the segmented VOP in 4*4 block-level accuracy is insufficient. Once we locate the target VOP, the actual edges of the VOP in 4*4 block accuracy can be extracted by applying Canny Edge Detection only on the moving VOP in 4*4 block accuracy. The VOP in pixel accuracy is then achieved by decompressing the DCT blocks of the VOPs. The edge-tracking algorithm is applied to find the missing edge pixels. After the segmentation process a retrieval algorithm that based on CSS (Curvature Scale Space) is used to search the interested shape of vehicle in H.264/AVC compressed video sequence. Experiments show that our algorithm can extract and retrieval moving vehicles efficiency and robustly.
Context-based Adaptive Binary Arithmetic Coding (CABAC) is a new entropy coding method presented in H.264/AVC that is highly efficient in video coding. In the method, the probability of current symbol is estimated by using the wisely designed context model, which is adaptive and can approach to the statistic characteristic. Then an arithmetic coding mechanism largely reduces the redundancy in inter-symbol. Compared with UVLC method in the prior standard, CABAC is complicated but efficiently reduce the bit rate. Based on thorough analysis of coding and decoding methods of CABAC, This paper proposed two methods, sub-table method and stream-reuse methods, to improve the encoding efficiency implemented in H.264 JM code. In JM, the CABAC function produces bits one by one of every syntactic element. Multiplication operating times after times in the CABAC function lead to it inefficient.The proposed algorithm creates tables beforehand and then produce every bits of syntactic element. In JM, intra-prediction and inter-prediction mode selection algorithm with different criterion is based on RDO(rate distortion optimization) model. One of the parameter of the RDO model is bit rate that is produced by CABAC operator. After intra-prediction or inter-prediction mode selection, the CABAC stream is discard and is recalculated to output stream. The proposed Stream-reuse algorithm puts the stream in memory that is created in mode selection algorithm and reuses it in encoding function. Experiment results show that our proposed algorithm can averagely speed up 17 to 78 MSEL higher speed for QCIF and CIF sequences individually compared with the original algorithm of JM at the cost of only a little memory space. The CABAC was realized in our progressive h.264 encoder.
It is a hot focus of current researches in video standards that how to transmit video streams over Internet and wireless
networks. One of the key methods is FGS(Fine-Granular-Scalability), which can always adapt to the network bandwidth
varying but with some sacrifice of coding efficiency, is supported by MPEG-4. Object-based video coding algorithm
has been firstly included in MPEG-4 standard that can be applied in interactive video. However, the real time
segmentation of VOP(video object plan) is difficult that limit the application of MPEG-4 standard in interactive video.
H.264/AVC is the up-to-date video-coding standard, which enhance compression performance and provision a network-friendly
video representation. In this paper, we proposed a new Object Based FGS(OBFGS) coding algorithm embedded
in H.264/AVC that is different from that in mpeg-4. After the algorithms optimization for the H.264 encoder, the FGS
first finish the base-layer coding. Then extract moving VOP using the base-layer information of motion vectors and
DCT coefficients. Sparse motion vector field of p-frame composed of 4*4 blocks, 4*8 blocks and 8*4 blocks in base-layer
is interpolated. The DCT coefficient of I-frame is calculated by using information of spatial intra-prediction. After
forward projecting each p-frame vector to the immediate adjacent I-frame, the method extracts moving VOPs (video
object plan) using a recursion 4*4 block classification process. Only the blocks that belong to the moving VOP in 4*4
block-level accuracy is coded to produce enhancement-layer stream. Experimental results show that our proposed
system can obtain high interested VOP quality at the cost of fewer coding efficiency.
In this paper, we proposed a new object-based coding algorithm by using wavelet transform to instead of the image encoder algorithm by using FGS in MPEG-4. The new object-based coding algorithm combines motion estimation with object-based 3-D wavelet transform for video coding in order to fully utilize the redundancy in the time domain. The shape-adaptive algorithm based on modifying boundary extension method of lifting scheme. A sequence of VOPs are fed into the motion compensated lifting (MCLIFT) wavelet coder which first decomposes the VOPs temporarily through MCLIFT filter, and then decompresses the VOPs spatially by shape adaptive lifting wavelet transform (SA-TWT). We encode the video and represent the stream as multilayer bit stream. The integrated transport-decoder buffer ensure the video be continuously transmitted. Losing package can be recovered by using re-transmission.
In this paper, we propose a new fast and efficient method of shape coding called feature point-based adaptive arithmetic encoding algorithm (FPAE). For dealing with video image, we regard a shape as a set of points that are parameterized by arc length and B-spline bases. Then, the evolution of curve at different resolution levels s in B-spline scale space is achieved by convoluting the curve with the dilated B-spline kernel instead of Gaussian kernel. Compared with Gaussian method, this method has an advantage of fast algorithm. By calculating the curvature, the feature-points including significant information of shape contour can be found. But for shape coding, this shape representation will not very efficient when the shape consist of arcs. The modified shape representation also includes the feature-point that lies in arc and the distance from the feature-point to chord is largest. All of the feature-points are encoded by adaptive arithmetic encoding. Experimental results show that our method reduces coded bits by about 25% compared with the context-based arithmetic encoding (CAE) of the MPEG-4 VM and the subjective quality of the reconstructed shape is better than that of CAE at same Dn.