Optimum nonnegative integer bit allocation for wavelet based signal compression and coding

Hatam, Mahdi; Masnadi-Shirazi, Mohammad Ali

doi:10.1016/j.ins.2014.11.019

Cited by 7 publications

(16 citation statements)

References 16 publications

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“…Now, the total RE variance is given by

σ_{normalr}^{2} = \sum_{i = 1}^{k} a_{i} 2^{- 2 b_{i}} σ_{o_{i}}^{2}

Finally, the OBA problem for a DCT‐ITC can be mathematically formulated as

\begin{matrix} right leftthickmathspace.5em \\ minimiz e_{b_{1}, b_{2}, \dots, b_{k}} \{σ_{r}^{2}\} Subject to : \frac{1}{k} \sum_{i = 1}^{k} b_{i} = B \\ b_{i} \in Ω, i = 1, 2, \dots, k \end{matrix}

From (5), we can simply frame the OBA objective for the DCT‐ITCs, as now the aim is to find the respective values of

b_{i}

for each of the quantisers to minimise (4) subjected to the constraint of (2). Consequently, we can define the following cases for the solutions of the OBA problem for the DCT‐ITC quantisers [25] (i) If the solution space

Ω = double-struckR

, then the OBA problem is considered as ORBA problem and its solution is termed as

{bold-italicb}_{normalr}^{*} = {[b_{r_{1}}^{*}, b_{r_{2}}^{*}, . . . b_{r_{k}}^{*}]}^{normalT} .

(ii) If the solution space

Ω = double-struckZ

, then the OBA problem is considered as OIBA problem and its solution is termed as

{bold-italicb}^{*} = false[b_{1}^{*}, b_{2}^{*}, \dots b_{k}

…”

Section: Oba Technique For the Dct‐itcsmentioning

confidence: 99%

“…The first ONIBA solution for the OBA problem is proposed by Fox [14] and then subsequently a low complexity ONIBA algorithm is proposed by Farber and Zeger [15]. Recently in 2015, Hatam and Shirazi [25] proposed a new low complexity ONIBA algorithm as compared to the existing algorithms. The detailed description of all these popular ONIBA algorithms can be seen in [14, 15, 25].…”

Section: Oba Technique For the Dct‐itcsmentioning

confidence: 99%

“…Some other techniques which addressed the OBA problem for the TCs are found in [16–24]. Recently in 2015, Hatam and Shirazi [25] proposed a new low complexity ONIBA algorithm, to achieve a similar bit allocation performance as the existing ONIBA algorithms. This ONIBA algorithm can also handle the zero source variance condition of the quantiser sources, which is the critical problem related to all the previous ONIBA algorithms.…”

Section: Introductionmentioning

confidence: 99%

“…The obtained quantisation table must be transmitted with the compressed bitstream as additional side information for the proper de‐quantisation at the receiver. This additional side information leads to an increase in the given bit budget for the transmission or storage [25]. (iii) The existing ONIBA algorithms are highly bitrate dependent, i.e.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Image‐independent optimal non‐negative integer bit allocation technique for the DCT‐based image transform coders

Thakur¹,

Thakur

Gupta³

et al. 2020

IET Image Processing

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“…Now, the total RE variance is given by

σ_{normalr}^{2} = \sum_{i = 1}^{k} a_{i} 2^{- 2 b_{i}} σ_{o_{i}}^{2}

Finally, the OBA problem for a DCT‐ITC can be mathematically formulated as

\begin{matrix} right leftthickmathspace.5em \\ minimiz e_{b_{1}, b_{2}, \dots, b_{k}} \{σ_{r}^{2}\} Subject to : \frac{1}{k} \sum_{i = 1}^{k} b_{i} = B \\ b_{i} \in Ω, i = 1, 2, \dots, k \end{matrix}

From (5), we can simply frame the OBA objective for the DCT‐ITCs, as now the aim is to find the respective values of

b_{i}

Ω = double-struckR

, then the OBA problem is considered as ORBA problem and its solution is termed as

{bold-italicb}_{normalr}^{*} = {[b_{r_{1}}^{*}, b_{r_{2}}^{*}, . . . b_{r_{k}}^{*}]}^{normalT} .

(ii) If the solution space

Ω = double-struckZ

, then the OBA problem is considered as OIBA problem and its solution is termed as

{bold-italicb}^{*} = false[b_{1}^{*}, b_{2}^{*}, \dots b_{k}

…”

Section: Oba Technique For the Dct‐itcsmentioning

confidence: 99%

Section: Oba Technique For the Dct‐itcsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Image‐independent optimal non‐negative integer bit allocation technique for the DCT‐based image transform coders

Thakur¹,

Thakur

Gupta³

et al. 2020

IET Image Processing

View full text Add to dashboard Cite

“…This wavelet packet transform can be achieved by two filter banks: low pass filter and high pass filter respectively [14]. The current work is focused to obtain the high energy information in the cascaded filter bank with its wavelet packet tree [15]. Figure 4 shows the block diagram of the extraction procedure of SBC feature.…”

Section: Subband Codingmentioning

confidence: 99%

A Novel Method for Indexing and Retrieval of Speech using Gaussian Mixture Model Techniques

Thiruvengatanadhan¹,

Dhanalakshmi²

2016

IJCA

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Large speech databases such a Television broadcasts, TV programs, radio broadcasts, CDs and DVDs are available online these days. Research related to speech indexing and retrieval has received much attention in recent days due to the huge multimedia data storage capabilities. The goal of speech indexing and retrieval system is to provide the user with capabilities to index and retrieve the speech archives in an efficient manner. In this paper, we propose a method for indexing and retrieval of the speech. The speech activity is identified using voice activitiy detection and each complete speech dialogue is separated into individual words by marking each word"s segment through the Root Means Square (RMS) energy envelope. Then the features namely Perceptual Linear Prediction (PLP), Power Normalized Cepstral Coefficient (PNCC), Subband Coding (SBC) and Sonogram extracted from each of the individual word. For retrieval, a novel method is proposed using Gaussian mixture models. The probability that the query feature vector belongs to the Gaussian is computed. The average Probability density function is computed for each of the feature vectors in the database and the retrieval is based on the highest probability.

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Improved Optimum Nonnegative Integer Bit Allocation Algorithm Using Fuzzy Domain Variance Estimation and Refinement for the Wavelet-Based Image Compression

Thakur

Gupta

et al. 2019

Circuits Syst Signal Process

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Optimum nonnegative integer bit allocation for wavelet based signal compression and coding

Cited by 7 publications

References 16 publications

Image‐independent optimal non‐negative integer bit allocation technique for the DCT‐based image transform coders

Image‐independent optimal non‐negative integer bit allocation technique for the DCT‐based image transform coders

A Novel Method for Indexing and Retrieval of Speech using Gaussian Mixture Model Techniques

Improved Optimum Nonnegative Integer Bit Allocation Algorithm Using Fuzzy Domain Variance Estimation and Refinement for the Wavelet-Based Image Compression

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