CABAC is an entropy coding in AVS2, which brings higher performance for coding. Because in the rate distortion optimization, we need to calculate every coding mode to get the number of bits encoded by entropy coding, ...
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ISBN:
(纸本)9781538651148
CABAC is an entropy coding in AVS2, which brings higher performance for coding. Because in the rate distortion optimization, we need to calculate every coding mode to get the number of bits encoded by entropy coding, and then we determine the optimal coding mode by comparing the distortion and rate between different modes. This process increases the complexity of the computation and prolongs the time of the coding. In order to reduce the complexity of rate distortion and optimize computation, this paper proposes a method based on syntax elements to estimates bit rate, which uses bit rate estimation instead of actual rate. The experimental results show that the average performance of high definition video (1080P) is reduced by 2.2% and the average coding time is 30.87%.
The promotion and application of HEVC face a great problem: how to reduce the coding complexity while ensuring the video compression rate. In order to reduce the complexity of renormalization, a fast renormalization a...
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ISBN:
(数字)9781538681398
ISBN:
(纸本)9781538681404
The promotion and application of HEVC face a great problem: how to reduce the coding complexity while ensuring the video compression rate. In order to reduce the complexity of renormalization, a fast renormalization algorithm is designed for the entropy coding module. This algorithm can perform renormalization and bit output without complex cyclic processes. At the meanwhile, an appropriate hardware structure is proposed and realized for the algorithm. Experimental results show that the design can reduce the renormalized frequency of 20.8%-25.6% and save the encoding time of 13.6%-30.9%. Moreover, the encoding rate can be up to 89 Mbin/s.
This paper presents a WaveNet-based zero-delay lossless speech coding technique for high-quality communications. The WaveNet generative model, which is a state-of-the-art model for neural-network-based speech waveform...
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ISBN:
(纸本)9781538643341
This paper presents a WaveNet-based zero-delay lossless speech coding technique for high-quality communications. The WaveNet generative model, which is a state-of-the-art model for neural-network-based speech waveform synthesis, is used in both the encoder and decoder. In the encoder, discrete speech signals are losslessly compressed using sample-by-sample entropy coding. The decoder fully reconstructs the original speech signals from the compressed signals without algorithmic delay. Experimental results show that the proposed coding technique can transmit speech audio waveforms with 50% their original bit rate and the WaveNet-based speech coder remains effective for unknown speakers.
Actually, the HEVC (High Efficiency Video coding) is becoming the most important consumer application platforms. Compared with its predecessor AVC (advanced video codec) standard, it can support the UHD (Ultra High De...
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ISBN:
(纸本)9781538668665
Actually, the HEVC (High Efficiency Video coding) is becoming the most important consumer application platforms. Compared with its predecessor AVC (advanced video codec) standard, it can support the UHD (Ultra High Definition) picture at 120 fps. H.265 adopts many advanced techniques such as SAO (Sample Adaptive Offset) filter, intra and inter prediction and CABAC entropy coding. CABAC (Context-based adaptive binary arithmetic coding) is adopted by AVC and HEVC standards. In H.265 CABAC, the bypassed syntax elements have direct impact on the total bite rate. This point is verified, estimated and simulated in our work at the small unit level on MATLAB software. Moreover, we propose hardware design of multiple bypass bins H.265 CABAC encoder. This architecture is implemented on different FPGA devices. The synthesis results demonstrate that our proposed architecture can process 1 to 5 bypass bins per cycle to ensure a good compromise between the throughput and the hardware cost.
This paper presents a novel method for classification of blocks into smooth and edge blocks in transform domain and a compression scheme for Wireless Capsule Endoscopy (WCE) with block classifier. WCE involves capturi...
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ISBN:
(数字)9781728120942
ISBN:
(纸本)9781728120959
This paper presents a novel method for classification of blocks into smooth and edge blocks in transform domain and a compression scheme for Wireless Capsule Endoscopy (WCE) with block classifier. WCE involves capturing, transmission and processing of gastrointestinal images. Power consumption is a critical issue in WCE, as it uses a button battery driven capsule endoscope to capture and transmit images. The captured image needs to be compressed to save the transmission power and low complexity compressor should be used to avoid more power consumption from the compressor itself. JPEG based compression techniques which consists Discrete Cosine Transform(DCT), quantizer and entropy encoder provides the best compression performance with less complexity compared to other various techniques. Pixel distribution in smooth blocks is uniform and energy is compacted only into low frequency bands in spectral domain. Because high frequency bands are almost having zero energy, only low frequency bands are quantized and entropy coded which saves power in processing high bands. Most of the endoscopic image has smooth region, this method is more suitable to WCE. Proposed algorithm improves compression rate by 9% without sacrificing quality compared to JPEG based compression algorithm.
Image compression standards rely on predic coding. transform coding, quantization and entropy coding, in order to achieve high compression performance. Very recently, deep generative models have been used to optimize ...
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ISBN:
(纸本)9781538660706
Image compression standards rely on predic coding. transform coding, quantization and entropy coding, in order to achieve high compression performance. Very recently, deep generative models have been used to optimize or replace some of these operations, with very promising results. However. so far no systematic and independent study of the coding performance of these algorithms has been carried out. In this paper, for the first time, we conduct a subjective evaluation of two recent deep learning-based image compression algorithnts, comparing them to JPEG 2000 and to the recent BPG image codec based on HEVC Intra. We found that compression approaches based on deep auto -encoders can achieve coding performance higher than JPEG 2000, and sometimes as good as BPG. We also show experimentally that the PSNR metric to be avoided when evaluating the visual quality of deep -learning-based methods, as artifacts have different characteristics from those of DCT or wavelet -based codecs. In particular, images compressed at low biirate appear more natural than JPEG 2000 coded pictu according to a no -reference naturalness measure. Our study indicates that deep generative models are likely to bring huge innovation into the video coding arena in the coming years.
We propose the first practical learned lossless image compression system, L3C, and show that it outperforms the popular engineered codecs, PNG, WebP and JPEG2000. At the core of our method is a fully parallelizable hi...
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ISBN:
(纸本)9781728132945
We propose the first practical learned lossless image compression system, L3C, and show that it outperforms the popular engineered codecs, PNG, WebP and JPEG2000. At the core of our method is a fully parallelizable hierarchical probabilistic model for adaptive entropy coding which is optimized end-to-end for the compression task. In contrast to recent autoregressive discrete probabilistic models such as PixelCNN, our method i) models the image distribution jointly with learned auxiliary representations instead of exclusively modeling the image distribution in RGB space, and ii) only requires three forward-passes to predict all pixel probabilities instead of one for each pixel. As a result, L3C obtains over two orders of magnitude speedups when sampling compared to the fastest PixelCNN variant (Multiscale-PixelCNN). Furthermore, we find that learning the auxiliary representation is crucial and outperforms predefined auxiliary representations such as an RGB pyramid significantly.
The most recent video compression technology is High Efficiency Video coding (HEVC). This soon to be completed standard is a joint development by Video coding Experts Group (VCEG) of ITU-T and Moving Picture Experts G...
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ISBN:
(纸本)9780819492166
The most recent video compression technology is High Efficiency Video coding (HEVC). This soon to be completed standard is a joint development by Video coding Experts Group (VCEG) of ITU-T and Moving Picture Experts Group (MPEG) of ISO/IEC. As one of its major technical novelties, HEVC supports variable prediction and transform block sizes using the quadtree approach for block partitioning. In terms of entropy coding, the Draft International Standard (DIS) of HEVC specifies context-based adaptive binary arithmetic coding (CABAC) as the single mode of operation. In this paper, a description of the specific CABAC-based entropy coding part in HEVC is given that is related to block structures and transform coefficient levels. In addition, experimental results are presented that indicate the benefit of the transform-coefficient level coding design in HEVC in terms of improved coding performance and reduced complexity.
For the entropy coding of independent and identically distributed (i.i.d.) binary sources, variable-to-variable length (V2V) codes are an interesting alternative to arithmetic coding. Such a V2V code translates variab...
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For the entropy coding of independent and identically distributed (i.i.d.) binary sources, variable-to-variable length (V2V) codes are an interesting alternative to arithmetic coding. Such a V2V code translates variable length words of the source into variable length code words by employing two prefix-free codes. In this article, several properties of V2V codes are studied, and new concepts are developed. In particular, it is shown that the redundancy of a V2V code cannot be zero for a binary i.i.d. source {X) with 0 < p(X)(1) < 0.5. Furthermore, the concept of prime and composite V2V codes is proposed, and it is shown why composite V2V codes can be disregarded in the search for particular classes of minimum redundancy codes. Moreover, a canonical representation for V2V codes is proposed, which identifies V2V codes that have the same average code length function. It is shown how these concepts can be employed to greatly reduce the complexity of a search for minimum redundancy (size-limited) V2V codes.
This paper proposes a high-throughput and low-complexity decoder (D_LBAC) based on Logarithmic Binary Arithmetic coding (LBAC). It can easily implement multiple symbols decoding. The proposed scheme does not use multi...
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This paper proposes a high-throughput and low-complexity decoder (D_LBAC) based on Logarithmic Binary Arithmetic coding (LBAC). It can easily implement multiple symbols decoding. The proposed scheme does not use multiplication and division operations nor look up tables (LUTs). It has a simple algorithm structure and only requires additions and shift operations. Experimental results show that it has about 0.2-0.7 % bit-rate savings and can decode 3.5 symbols per cycle on average. The hardware implementation design described in this paper can achieve a high symbol processing capability and the lower hardware costs.
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