Embedded Quantizer Design for Low Rate Lossy Image Coding

Aulí-Llinàs, Francesc; Marcellin, Michael W.; Jimenez-Rodriguez, Leandro; Blanes, Ian; Serra-Sagristà, Joan

doi:10.1109/dcc.2012.17

Cited by 3 publications

(3 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Experiments validate our observations during quantizer design: the scheme of USDQ+BPC is near-optimal from the point of view of compression efficiency, though sub-optimal from the point of view of number of quantization stages. This paper extends our previous works [30], [31] with detailed descriptions and comparisons of the quantization schemes proposed, comprehensive evaluations of the theory and practice behind them, and an extended set of experimental results.…”

Section: Introductionmentioning

confidence: 60%

General Embedded Quantization for Wavelet-Based Lossy Image Coding

Aulí-Llinàs

2013

IEEE Trans. Signal Process.

Self Cite

View full text Add to dashboard Cite

Abstract-Embedded quantization is a mechanism employed by many lossy image codecs to progressively refine the distortion of a (transformed) image. Currently, the most common approach to do so in the context of wavelet-based image coding is to couple uniform scalar deadzone quantization (USDQ) with bitplane coding (BPC). USDQ+BPC is convenient for its practicality and has proved to achieve competitive coding performance. But the quantizer established by this scheme does not allow major variations. This paper introduces a multistage quantization scheme named general embedded quantization (GEQ) that provides more flexibility to the quantizer. GEQ schemes can be devised for specific decoding rates achieving optimal coding performance. Practical approaches of GEQ schemes achieve coding performance similar to that of USDQ+BPC while requiring fewer quantization stages. The performance achieved by GEQ is evaluated in this paper through experimental results carried out in the framework of modern image coding systems.

show abstract

Section: Introductionmentioning

confidence: 60%

General Embedded Quantization for Wavelet-Based Lossy Image Coding

Aulí-Llinàs

2013

IEEE Trans. Signal Process.

Self Cite

View full text Add to dashboard Cite

show abstract

“…Despite the popularity of USDQ+BPC, [20], [21] noted that such a quantization scheme is not specifically designed to achieve optimal coding performance for a selected range of decoding rates. Motivated by this issue, a more flexible quantization scheme in which each stage of the quantizer partitions only one of the previous intervals was introduced [20], [21].…”

Section: Introductionmentioning

confidence: 99%

“…Motivated by this issue, a more flexible quantization scheme in which each stage of the quantizer partitions only one of the previous intervals was introduced [20], [21]. The proposed scheme is called general embedded quantization (GEQ).…”

Section: Introductionmentioning

confidence: 99%

Low Complexity Embedded Quantization Scheme Compatible with Bitplane Image Coding

Aulí-Llinàs

2013

2013 Data Compression Conference

View full text Add to dashboard Cite

Embedded quantization is a mechanism through which image coding systems provide quality progressivity. Although the most common embedded quantization approach is to use uniform scalar deadzone quantization (USDQ) together with bitplane coding (BPC), recent work suggested that similar coding performance as that achieved with USDQ+BPC can be obtained with a general embedded quantization (GEQ) scheme than performs fewer quantization stages. Unfortunately, practical approaches of GEQ can not be implemented in bitplane coding engines without substantially modifying their structure. This work overcomes this drawback introducing a 2-step scalar deadzone quantization (2SDQ) scheme compatible with bitplane image coding that provides the same advantages of practical GEQ approaches. Herein, 2SDQ is introduced in the framework of JPEG2000 to demonstrate its viability and efficiency.

show abstract