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In this paper, we address the problem of efficient High Dynamic Range (HDR) video coding. For this purpose, we introduce a new representation which segments the HDR image into low-range and highrange regions which are independently tone mapped. This representation allows preserving more information, leading to a higher visual quality of the reconstructed HDR image.
In this paper, we proposed a novel coding scheme is proposed using wavelet-based CS framework for nature image. First, two-dimension discrete wavelet transform (DWT) is applied to a nature image for sparse representation. after multi-scale DWT, the low-frequency sub-band and high frequency sub-bands are re-sampled separately. According to the statistical dependences among DWT coefficients, we allocate...
The feasible implementation of immersive 3D video systems entails the need for a substantial reduction in the amount of image information necessary for representation. Multiview image rendering algorithms based on depth data have radically reduced the number of images required to reconstruct a 3D scene. Nonetheless, the compression of depth maps still poses several challenges due to the particular...
The distortion by using traditional video encoders (e.g. H.264) on the depth discontinuity can introduce disturbing effects on the synthesized view. The proposed scheme aims at preserving the most significant depth transition for a better view synthesis. Furthermore, it has a scalable structure. The scheme extracts edge contours from a depth image and represents them by chain code. The chain code...
Texture and depth maps of two neighboring camera viewpoints are typically required for synthesis of an intermediate virtual view via depth-image-based rendering (DIBR). However, the bitrate overhead required for reconstruction ofmultiple texture and depthmaps at decoder can be large. The performance of multiview video encoders such as MVC is limited by the simple fact that the chosen representation...
This paper proposes a novel image compression scheme based on the local feature descriptor - Scale Invariant Feature Transform (SIFT). The SIFT descriptor characterizes an image region invariantly to scale and rotation. It is used widely in image retrieval. By using SIFT descriptors, our compression scheme is able to make use of external image contents to reduce visual redundancy among images. The...
Block transform coding using discrete cosine transform is the most popular approach for image compression. However, many annoying blocking artifacts are generated due to coarse quantization on transform coefficients independently. This paper proposes an effective blocking artifacts reduction method by estimating the transform coefficients from their quantized version. In the proposed scheme, we estimate...
Depth images have different characteristics from that of color images. They usually have gradual changes within objects while steep changes happen around object boundaries. Compression standards such as H.264/AVC and High Efficiency Video Coding (HEVC) are efficient in dealing with the gradual change regions but usually result in poor performance at edge regions. To facilitate the reuse of the current...
Multiple Description Coding is an effective way to improve the robustness of real-time data transmission over unreliable channels. In this paper, we propose a novel MDC scheme based on rotation which is compatible with the current video standard. In the MDC approach, two or more independently decodable descriptions of the same data are generated. In the case of two descriptions, one of the descriptions...
Since the 3D coding and transmission standard is not available nowadays, we need to transfer the 3D contents through the existing coding standards. To achieve this, the most common way is to combine both left and right eye frames into one single frame, which is the so-called stereo packing. So far, there are many packing methods and each of them has different characteristics and advantages. To obtain...
Predictor based algorithms reported in literature uses only causal pixels and hence a non-symmetrical predictor structure for prediction. We observed that the performance of predictor is highly dependent on the predictor structure used. In view of this, we propose a novel interpolation based prediction scheme that enables us to use symmetrical predictor structure. In this sense, we have also used...
We consider the problem of recovering a set of correlated signals (e.g., images from different viewpoints) from a few linear measurements per signal. We assume that each sensor in a network acquires a compressed signal in the form of linear measurements and sends it to a joint decoder for reconstruction. We propose a novel joint reconstruction algorithm that exploits correlation among underlying signals...
A scalable multiple description scalar quantizer (SMDSQ) is a quantization based framework used for scalable multiple description coding (SMDC). In this paper, we introduce a novel generalization of the Lloyd-Max algorithm to realize locally optimal SMDSQs. Both level-constrained and entropy-constrained cases are considered. For both cases, locally optimal solutions are realized by iterative execution...
We present a video compression scheme using epitome based texture coding that uses a low quality video as side information and improves it by using the epitome. The side information is sent at different levels of quantization and resolution to optimize the quality against the bit rate. The concept of motion threading is used for propagation of epitome information from one frame to another. The proposed...
In this paper we present a video coding scheme based on texture synthesis through Directional Empirical Mode Decomposition (DEMD). In this scheme P and B-frames of the video sequence are decomposed and parametrically coded with the help of DEMD algorithm, while I-frames are coded with the help of H.264. All P and B frames are decomposed into Intrinsic Mode Function (IMF) image and its residue. Only...
Surveillance video privacy protection has drawn significant attention recently. In this paper, we describe a privacy protected video surveillance system which utilizes the emerging compressive sensing (CS) theory. Privacy regions are scrambled through block based CS sampling on quantized coefficients during compression. Security is ensured by key controlled chaotic sequence which is used to construct...
Compressive sensing has emerged as an important new technique in signal acquisition due to the surprising property that a sparse signal can be captured from measurements obtained at a sub-Nyquist rate. The decoding cost of compressive sensing, however, grows superlinearly with the problem size. In distributed sensor systems, the aggregate amount of compressive measurements encoded by the sensors can...
We propose a new algorithm for improving the coding efficiency of bit-depth scalable video coding. While coding the enhancement layers in bit-depth scalable coding under scalable video coding framework, we apply a correction to the error residuals generated after the inverse tone mapping process. The correction factor is towards reducing the residual values close to zero and this leads to reduction...
This paper proposes an improved Distributed Compressive Video Sensing (DCVS) framework based on adaptive sparse basis, which integrates the recently emerging Distributed Video Coding (DVC) and Compressive Sensing (CS) theory. The proposed framework incorporates a low-complexity encoder and shifts most computation burden to the decoder-side. At the encoder, the video frames are sampled independently...
Traditional video coding method is able to achieve wonderful performance in data compressing. However, it has high complexity, which is not suitable for some environments where low complexity coding is needed. A new method of video coding which is based on compressive sensing is proposed. In this system, the sparsity of residual of successive frames is exploited, which is a crucial requirement in...
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