本文選題：壓縮感知 + 壓縮感知視頻�。� 參考：《南京郵電大學(xué)》2016年博士論文

【摘要】：在現(xiàn)今信息技術(shù)大發(fā)展時代,以圖像視頻為載體的服務(wù)應(yīng)用在信息傳輸中占據(jù)重要地位,與此同時圖像視頻處理傳輸技術(shù)得到了普遍關(guān)注。以H.264為代表的成熟且高效的預(yù)測類視頻編碼方法是圖像視頻處理的基礎(chǔ),但是,由于預(yù)測類編碼所需計算量巨大,并且會導(dǎo)致信道誤差影響延續(xù),不適用于一些資源有限、低耗要求的無線應(yīng)用場景,如參與式感知、無線多媒體傳感器網(wǎng)絡(luò)等。壓縮感知(CS)理論為解決上述問題提供了一種新的思路。該理論不僅在采樣速率方面突破了奈奎斯特頻率的限制,而且在采樣觀測的同時以降維的方式實現(xiàn)了圖像或視頻數(shù)據(jù)一定程度上的壓縮,實現(xiàn)了采樣和壓縮兩個過程的合并,避免了傳統(tǒng)先采樣后壓縮所產(chǎn)生的資源耗費。而且,壓縮感知作為一種用信息全局觀測(即所謂信息采樣)代替?zhèn)鹘y(tǒng)的信號局部采樣(即所謂信號采樣)的新型信號采集方式,通過線性隨機投影所獲得的觀測值承載了具有相同重要性的信號整體信息,有利于構(gòu)造簡單有效的抗信道誤差方案。壓縮感知理論與現(xiàn)有視頻技術(shù)結(jié)合,可有效緩解高速采樣實現(xiàn)的壓力,減少節(jié)點采集、編碼、處理和傳輸?shù)某杀?將會進一步推動信息領(lǐng)域的向前發(fā)展。本論文在壓縮感知理論的基礎(chǔ)上,以圖象/視頻信號為主要研究對象,緊緊圍繞視頻質(zhì)量評價、編碼效率提高和圖像/視頻差錯控制三個方面中與視頻傳輸質(zhì)量有關(guān)的關(guān)鍵問題展開研究。論文的主要研究和創(chuàng)新性成果如下:首先,在視頻質(zhì)量評價方面,提出一種基于冗余觀測值的部分參考CS視頻質(zhì)量評價方法和一種體現(xiàn)主觀感知質(zhì)量特性的客觀分層CS視頻質(zhì)量評價方法。前者以客觀和部分參考的形式,以較低的附加冗余觀測值的成本實現(xiàn)了對CS視頻恢復(fù)后的質(zhì)量評價,所獲質(zhì)量信息與PSNR值具有較強的相關(guān)性;同時,依據(jù)所獲視頻質(zhì)量信息,實現(xiàn)CS視頻質(zhì)量信息反饋觀測率的自適應(yīng)調(diào)整方案,與固定觀測率CS視頻傳輸相比,視頻整體質(zhì)量得到有效提高。后者評價模型分別從觀測層、流層和分組層次描述CS視頻網(wǎng)絡(luò)傳輸參數(shù)對視頻質(zhì)量的影響,這種分層模型可根據(jù)應(yīng)用需求提供不同層的CS視頻質(zhì)量信息,而且模型參數(shù)通過主觀CS視頻質(zhì)量數(shù)據(jù)回歸分析獲得;所獲結(jié)果具有人類主觀感受特征,而且由該模型所獲的CS視頻質(zhì)量信息與驗證對比實驗獲得的主觀CS視頻質(zhì)量信息具有強相關(guān)性。其次,在提高編碼效率方面,針對CS量化編碼,提出一種觀測值刪除量化方法。該方法從觀測值特征分析出發(fā),考慮觀測值全局投影和近似高斯分布的性質(zhì),通過觀測值刪除來縮減觀測值值域范圍,從而在一定的量化電平數(shù)量下減少量化誤差、提高觀測值量化率失真性能。本方法在適合的刪除參數(shù)下,CS圖像重建質(zhì)量優(yōu)于直接均勻標(biāo)量量化方法和分塊圖像壓縮感知DPCM方法。最后,在傳輸差錯控制方面,提出一種基于顯著性信息的可分級編碼差錯控制方法和一種基于單比特奇偶校驗的檢錯刪除差錯控制方法。前者針對大數(shù)據(jù)量圖像信號單路徑傳輸?shù)牡涂煽啃?依據(jù)多徑分集和圖像顯著性分析技術(shù),實現(xiàn)針對CS圖像的可分級編碼這種不對稱信道保障方法;該方法在無差錯環(huán)境中率失真性能優(yōu)于傳統(tǒng)無顯著性信息方法,在丟包環(huán)境下率失真性能優(yōu)于CS多描述編碼方法。后者從CS視頻傳輸觀測值結(jié)構(gòu)特征出發(fā),以在觀測值數(shù)據(jù)段增加單比特奇偶校驗位的檢錯刪除方式實現(xiàn)對CS視頻的差錯控制,此方法實現(xiàn)簡易且參數(shù)可根據(jù)信道狀態(tài)調(diào)整,可在不同誤比特率信道條件下,獲得接近BCH碼和RCPC碼的性能,而且在碼率和計算復(fù)雜度上具有明顯優(yōu)勢。
[Abstract]:In the era of information technology development, the application of image video as the carrier occupies an important position in the information transmission. At the same time, the image video processing and transmission technology has received widespread attention. The mature and efficient prediction video coding method represented by H.264 is the basis of the image video processing, but the prediction class is made up. The code needs a huge amount of computation, and it will cause the channel error to affect the continuity. It is not suitable for some wireless application scenarios such as participatory perception and wireless multimedia sensor network, such as participatory perception and wireless multimedia sensor network (CS). The theory provides a new way of thinking to solve the above problems. The limitation of Nyquist frequency, and the compression of image or video data to a certain extent in the way of dimensionality reduction at the same time of sampling observation, realizes the merging of the two processes of sampling and compression, and avoids the resource consumption produced by the traditional pre sampling compression. Moreover, the compression perception is used as a global observation of information. Information sampling) instead of the traditional signal local sampling (the so-called signal sampling) new signal acquisition mode, the observed values obtained by linear random projection carry the whole information with the same importance, which is beneficial to the construction of a simple and effective anti channel error scheme. Reducing the pressure of high-speed sampling, reducing the cost of node acquisition, coding, processing and transmission, will further promote the development of information field. On the basis of compressed sensing theory, this paper focuses on image / video signal as the main research object, closely surrounding video quality evaluation, coding efficiency and image / video error control. In three aspects, the key issues related to video transmission quality are studied. The main research and innovative results of this paper are as follows: firstly, a partial reference CS video quality evaluation method based on redundant observation values and an objective hierarchical CS video quality evaluation based on subjective perceptual quality characteristics are proposed. In the form of objective and partial reference, the former realizes the quality evaluation of CS video recovery with the cost of lower additional redundant observations. The quality information has a strong correlation with the PSNR value. At the same time, according to the video quality information obtained, the adaptive adjustment scheme of the feedback observation rate of the CS video quality information is realized. Compared with the fixed observation rate CS video transmission, the overall quality of the video is effectively improved. The latter model describes the effect of the CS video network transmission parameters on the video quality from the observation layer, the flow layer and the packet level respectively. This hierarchical model can provide the CS video quality information of different layers according to the application requirements, and the model parameters pass through the subjective CS. The result of video quality data regression analysis is obtained; the results obtained have human subjective feelings, and the CS video quality information obtained by the model has strong correlation with the subjective CS video quality information obtained by the verification contrast experiment. Secondly, in the aspect of improving the coding efficiency, a method of observation value deletion and quantization is proposed for the CS quantization coding. This method, starting from the analysis of observation value features, takes into account the global projection of observation values and the properties of approximate Gauss distribution, and reduces the range of observation value range by observation value deletion, thus reducing the quantization error and improving the distortion performance of the observed value quantization rate under certain quantized level. Under the suitable deletion parameters, the CS image is reconstructed. The quantity is superior to the direct uniform scalar quantization method and the block image compression sensing DPCM method. Finally, in the aspect of transmission error control, a scalable coding error control method based on significant information and a error detection and deletion control method based on single bit parity check are proposed. The former aims at the single path transmission of large data amount image signals. The low reliability of the transmission is based on the multipath diversity and image saliency analysis technology to achieve an asymmetric channel guarantee method for CS images. The rate distortion performance in the error free environment is superior to the traditional non significant information method. In the packet loss environment, the rate loss is superior to the CS multi description coding method. The latter is viewed from CS. The error control of CS video is realized by adding the error detection and deletion method of the single bit parity bit in the observation data segment. This method is simple and parameters can be adjusted according to the channel state. Under the condition of different bit error rate channels, the performance is obtained near the BCH code and RCPC code, and the bit rate and the rate are also available. The computational complexity has obvious advantages.
【學(xué)位授予單位】：南京郵電大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2016
【分類號】：TN919.8

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