We propose an efficient coding scheme for a dense light field, i.e., a set of multi-viewpoint images taken with very small viewpoint intervals. The key idea behind our proposal is that a light field is represented onl...
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ISBN:
(纸本)9781479970612
We propose an efficient coding scheme for a dense light field, i.e., a set of multi-viewpoint images taken with very small viewpoint intervals. The key idea behind our proposal is that a light field is represented only using weighted binary images, where several binary images and corresponding weight values are to be chosen to optimally approximate the light field. The coding scheme derived from this idea is completely different from those of modern image/video coding standards. However, we found that our scheme can achieve comparable coding efficiency (rate-distortion performance) to that of modern highly-sophisticated video codecs. Moreover, the decoding process of our scheme is extremely simple, which will lead to a faster and less power-hungry decoder than those of the modern codecs. Furthermore, our scheme can be made scalable, where the accuracy of the decoded light field is improved in a progressive manner as we use more encoded information. Thanks to the divide-and-conquer strategy adopted for the scalable coding, we can also drastically reduce the computational complexity of the encoding process.
This paper describes a novel scalable 3D triangular mesh coding method based on wavelet transform and successive approximation quantization. The algorithm efficiently exploits the intracorrelations between wavelet coe...
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This paper describes a novel scalable 3D triangular mesh coding method based on wavelet transform and successive approximation quantization. The algorithm efficiently exploits the intracorrelations between wavelet coefficients independently in each subband. Non-significant wavelet coefficients are clustered, in a per bit-plane manner, by using an octree-based approach. An hierarchical bitstream is then generated allowing to gradually decode the 3D mesh at the desired quality or resolution representation. Our proposal can be executed on arbitrary topology meshes by using irregular wavelet decomposition. Objective and subjective quality evaluation on representative 3D meshes shows that the proposed codec provides competitive compression results when compared to the state-of-the-art. Furthermore, it fits well to applications that require fast interactive handling of highly detailed 3D meshes, over networks with limited and/or variable bandwidth.
We propose a wavelet-based codec for the static depth-image-based representation, which allows viewers to freely choose the viewpoint. The proposed codec jointly estimates and encodes the unknown depth map from multip...
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We propose a wavelet-based codec for the static depth-image-based representation, which allows viewers to freely choose the viewpoint. The proposed codec jointly estimates and encodes the unknown depth map from multiple views using a novel rate-distortion (RD) optimization scheme. The rate constraint reduces the ambiguity of depth estimation by favoring piece-wise-smooth depth maps. The optimization is efficiently solved by a novel dynamic programming along trees of integer wavelet coefficients. The codec encodes the image and the depth map jointly to decrease their redundancy and to provide a RD-optimized bitrate allocation between the two. The codec also offers scalability both in resolution and in quality. Experiments on real data show the effectiveness of the proposed codec.
Compression of encrypted data draws much attention in recent years due to the security concerns in a service-oriented environment such as cloud computing. We propose a scalable lossy compression scheme for images havi...
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Compression of encrypted data draws much attention in recent years due to the security concerns in a service-oriented environment such as cloud computing. We propose a scalable lossy compression scheme for images having their pixel value encrypted with a standard stream cipher. The encrypted data are simply compressed by transmitting a uniformly subsampled portion of the encrypted data and some bitplanes of another uniformly subsampled portion of the encrypted data. At the receiver side, a decoder performs content-adaptive interpolation based on the decrypted partial information, where the received bit plane information serves as the side information that reflects the image edge information, making the image reconstruction more precise. When more bit planes are transmitted, higher quality of the decompressed image can be achieved. The experimental results show that our proposed scheme achieves much better performance than the existing lossy compression scheme for pixel-value encrypted images and also similar performance as the state-of-the-art lossy compression for pixel permutation-based encrypted images. In addition, our proposed scheme has the following advantages: at the decoder side, no computationally intensive iteration and no additional public orthogonal matrix are needed. It works well for both smooth and texture-rich images.
This paper describes the implementation of the recently introduced color set partitioning in hierarchical tree (CSPIHT)-based scheme for video coding. The intra- and interframe coding performance of a CSPIHT-based vid...
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This paper describes the implementation of the recently introduced color set partitioning in hierarchical tree (CSPIHT)-based scheme for video coding. The intra- and interframe coding performance of a CSPIHT-based video coder (CVC) is compared against that of the H.263 at bit rates lower than 64 kbit/s. The CVC performs comparably or better than the H.263 at lower bit rates, whereas the H.263 performs better than the CVC at higher bit rates. We identify areas that hamper the performance of the CVC and propose an improved scheme that yields better performance in image and video coding in low bit-rate environments.
In this paper, we first briefly discuss the newly emerging Secured JPEG (JPSEC) standard for security services for JPEG 2000 compressed images. We then propose our novel approach for applying authentication to JPEG 20...
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In this paper, we first briefly discuss the newly emerging Secured JPEG (JPSEC) standard for security services for JPEG 2000 compressed images. We then propose our novel approach for applying authentication to JPEG 2000 images in a scalable manner. Our authentication technique can be used for source authentication, nonrepudiation and integrity verification for the received possibly transcoded JPEG 2000 images in such a way that it is possible to authenticate different resolutions or different qualities extracted or received from a JPEG 2000 encoded image. Three different implementation methods for our authentication technique are presented. Packet-Based Authentication involves using the MD5 hashing algorithm for calculating the hash value for each individual packet in the JPEG 2000 codestream. Hash values are truncated to a specified length to reduce the overhead in storage space, concatenated into a single string, and then signed using the RSA algorithm and the author's private key for repudiation;prevention. Resolution-Based Authentication and Quality-Based Authentication methods involve generating a single hash value from all contiguous packets from each entire resolution or each entire quality layer, respectively. Our algorithms maintain most of the inherent flexibility and scalability of JPEG 2000 compressed images. The resultant secured codestream is still JPEG 2000 compliant and compatible with JPEG 2000 compliant decoders. Also, our algorithms are compatible with the Public Key Infrastructure (PKI) for preventing signing repudiation from the sender and are implemented using the new JPSEC standard for security signaling.
This paper presents a new scalable locally adaptive resolution lossless low-complexity (LAR-LLC) image codec. It is based on the LAR framework that is a multiresolution compression method supporting both lossy and los...
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This paper presents a new scalable locally adaptive resolution lossless low-complexity (LAR-LLC) image codec. It is based on the LAR framework that is a multiresolution compression method supporting both lossy and lossless coding. To achieve an efficient low-complexity solution, each processing stage of the LAR is modified. For the first step, consisting of a pyramidal decomposition, a new reversible transform called hierarchical diagonal S transform (HD-ST) is proposed. The HD-ST operates on sets of data pairs, requiring only shift and add/sub operations. The second step performs the prediction of the transformed coefficients. The prediction scheme considers both inter-and intra-level information, and involves fixed weights. Then, a classification process is introduced to separate prediction errors into subclasses, using a context modeling approach. Finally, each subclass is coded by the Huffman coding algorithm. The results of the lossless compression experiments showed that LAR-LLC achieves the same compression performance as JPEG2000 with a lower complexity.
This paper investigates the relationship between rate-distortion theory and efficient content-based data retrieval from high-dimensional databases. We consider database design as the encoding of a data object sequence...
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This paper investigates the relationship between rate-distortion theory and efficient content-based data retrieval from high-dimensional databases. We consider database design as the encoding of a data object sequence, and retrieval from the database as the decoding of the sequence using side information (i.e., the query) available only at the decoder. We show that, in this setting, the optimal asymptotic tradeoff between the search time R-s (bits per data object read from the storage device) and the expected search accuracy D-s (relevance of the retrieved data set) is given by the Wyner-Ziv solution with a side-information-dependent distortion measure. Moreover, the data indexing and retrieval problem is, in general, inseparable from the data compression problem. Data items selected by the search procedure, which can be stored in the disk with a limited total rate of R-r greater than or equal to R-s, need to be presented at a prescribed expected reconstruction quality D-r. This is, hence, a problem of scalable source coding or successive refinement, albeit with differing layer distortion measures to quantify search and reconstruction quality, respectively. We derive a single-letter characterization of all achievable quadruples {R-s, R-r, D-s, D-r}, and prove conditions for "successive refinability" without rate loss. Finally, we show that the special case D-s = D-r = 0 is nontrivial and of practical interest in this context, as it can impose "acceptable" search and reconstruction qualities for each individual data item and for the entire query space with high probability, in contradistinction with standard average distortion requirements. The region of achievable {R-s,R- R-r} is obtained by adapting Rimoldi's characterization to a new regular scalable coding problem.
Image compression aims to minimize the amount of data in image representation while maintaining a certain visual quality for humans, which is an essential technique for storage and transmission. Recently, along with t...
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Image compression aims to minimize the amount of data in image representation while maintaining a certain visual quality for humans, which is an essential technique for storage and transmission. Recently, along with the development of computer vision, machines have become another primary receiver for images and require compressed images at a certain quality level, which may be different from that of human vision. In many scenarios, compressed images should serve both human and machine vision tasks, but few compression methods are designed for both goals simultaneously. In this article, we propose a unified and scalable deep image compression (USDIC) framework that jointly optimizes the image quality according to human and machine vision in an end-to-end style. For the encoder, we propose an information splitting mechanism (ISM) to separate images into semantic and visual features, which mainly aims at machine analysis and human viewing tasks. For the decoder, we design a scalable decoding architecture. The encoded semantic feature is first decoded for machine analysis tasks, and the image is decoded and reconstructed further by leveraging the decoded semantic features. Herein, to further remove the redundancy between the semantic and visual features of images, we propose a scalable entropy model (SEM) with a joint optimization strategy to reconstruct the image using the two kinds of decoded features. Extensive experimental results show that the proposed USDIC achieves much better performance on the image analysis task while maintaining competitive performance on the traditional image reconstruction task compared with popular image compression methods.
This paper proposes a novel scalable compression method for stream cipher encrypted images, where stream cipher is used in the standard format. The bit stream in the base layer is produced by coding a series of nonove...
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This paper proposes a novel scalable compression method for stream cipher encrypted images, where stream cipher is used in the standard format. The bit stream in the base layer is produced by coding a series of nonoverlapping patches of the uniformly down-sampled version of the encrypted image. An off-line learning approach can be exploited to model the reconstruction error from pixel samples of the original image patch, based on the intrinsic relationship between the local complexity and the length of the compressed bit stream. This error model leads to a greedy strategy of adaptively selecting pixels to be coded in the enhancement layer. At the decoder side, an iterative, multiscale technique is developed to reconstruct the image from all the available pixel samples. Experimental results demonstrate that the proposed scheme outperforms the state-of-the-arts in terms of both rate-distortion performance and visual quality of the reconstructed images at low and medium rate regions.
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