Layered multiple description video coding using dual-tree discrete wavelet transform and H.264/AVC

Chen, Jing; Cai, Canhui; Li, Li; Li, Cuihua

doi:10.1007/s11042-015-2546-0

Cited by 7 publications

(4 citation statements)

References 16 publications

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“…For instance, an H.264-compliant MD video coding is achieved by interlacing primary and redundant slices [3], whose rate distortion is optimized in an end-to-end manner. In [18], a hybrid layered MD video coding algorithm uses the H.264/AVC encoder to compress a video at low bit rates as the base layer, and then, four-bit streams are divided into two groups as two description enhancement layers after 3D dual-tree discrete wavelet transform and 3D-SPIHT encoding. For HEVC-compatible MD video coding, the redundancy allocation is assigned based on a visual saliency model [10].…”

Section: Standard-compliant Multiple Description Codingmentioning

confidence: 99%

“…Most recently, image representation-based compression with CNNs [11] has been extended for multiple description image generation to form a standard-compliant convolutional neural network-based MDC framework [2], which is trainable in an end-to-end fashion. Although great progresses have been achieved for the class of standard-compliant multiple description coding [3,10,18], their coding efficiency has great potential to be enormously improved by artificial neural networks in various ways such as pre-filtering and post-processing [2,11,26].…”

Section: Standard-compliant Multiple Description Codingmentioning

confidence: 99%

“…Different from multiple description quantizers and the correlated transform-based MDC framework, a class of sampling-based multiple description coding is more flexible and compatible with the standard coders. However, most of existing sampling-based MDC methods are built on the specifically designed sampling methods or extend the existing sampling operator for multiple description generations [3,10,11,18], whose coding efficiency is limited. Consequently, the research of sampling-based MDC methods should be further developed.…”

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confidence: 99%

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Deep Multiple Description Coding by Learning Scalar Quantization

Zhao

Bai

Wang

et al. 2019

2019 Data Compression Conference (DCC)

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In this paper, we introduce a deep multiple description coding (MDC) framework optimized by minimizing multiple description (MD) compressive loss. First, MD multi-scale-dilated encoder network generates multiple description tensors, which are discretized by scalar quantizers, while these quantized tensors are decompressed by MD cascaded-ResBlock decoder networks. To greatly reduce the total amount of artificial neural network parameters, an auto-encoder network composed of these two types of network is designed as a symmetrical parameter sharing structure. Second, this autoencoder network and a pair of scalar quantizers are simultaneously learned in an end-to-end self-supervised way. Third, considering the variation in the image spatial distribution, each scalar quantizer is accompanied by an importance-indicator map to generate MD tensors, rather than using direct quantization. Fourth, we introduce the multiple description structural similarity distance loss, which implicitly regularizes the diversified multiple description generations, to explicitly supervise multiple description diversified decoding in addition to MD reconstruction loss. Finally, we demonstrate that our MDC framework performs better than several state-of-the-art MDC approaches regarding image coding efficiency when tested on several commonly available datasets.

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Section: Standard-compliant Multiple Description Codingmentioning

confidence: 99%

Section: Standard-compliant Multiple Description Codingmentioning

confidence: 99%

mentioning

confidence: 99%

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Deep Multiple Description Coding by Learning Scalar Quantization

Zhao

Bai

Wang

et al. 2019

2019 Data Compression Conference (DCC)

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“…Else, secret data hiding is accomplished in frequency domain where signature has to be embedded into frequency coefficients of an input video. Some of renovations are conceivable such as discrete cosine transforms [1], discrete Fourier transforms [15] and discrete wavelet transforms [14]. The H.264/AVC standard turns to be most extensively organized video codec method for majority of application due to which several watermarking techniques are designed.…”

Section: Introductionmentioning

confidence: 99%

Reversible and Tunable Data Hiding with Advanced Video Coding

Vivek*,

Gadgay

2020

IJRTE

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Due to broad usage of digital broadcasting application, information can be hackneyed and duplicated. Hence, multimedia security & copy right protection is becoming crucial. Steganography is an outstanding apparatus in providing protection as well as security to multimedia information by hiding digital content. H.264/Advanced Video Coding (AVC), a recent international standardized video coding technique which is under expansion, has presented a major revolution in video compression efficiency. The encoding efficiency of AVC is sophisticated than previous ethics such as H.263. In this paper, a complexity video embedding on H.264 is employed. So as to hide the secret image, chaos image encryption technique has been used where shuffling positions and altering grey values of image pixels are done. The secret image is embedded into video sequences by varying coefficients of I and P Frames. In decoding process, embedded information can be extracted without loss of compressed video content. Applications of proposed method include medicine, military where loss of carrier data is not permitted. Proposed model affords an overview of technical features and relative characteristics of H.264 standard that gives good PSNR and embedding capacity.

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