Entropy coder for audio signals

In the paper an effective entropy coder designed for coding of prediction errors of audio signals is presented. The coder is implemented inside a greater structure which signal modeling part is a lossless coding backward adaptation algorithm consisting of cascaded OLS and NLMS sections is presented. The technique performance is compared to that of 4 other lossless codecs, including MPEG-4 ALS one, and it is shown that indeed, the new method is the best one. The entropy coder is an advanced context adaptive Golomb one followed by two context adaptive arithmetic coders

Novel Ideas for Lossless Audio Coding

Novel ideas for lossless audio coding analyzed in the paper are linked with forward predictor adaptation, and concern optimization of predictors on the basis of zero-orderentropy and MMAE criterions, and context sound coding. Direct use of the former criterion is linked with exponential growth of optimization procedure, hence, a suboptimal algorithm having polynomial complexity is proposed. It is shown that on average the new types of predictors are better than those obtained by MMSE technique, while two- and three context systems are on average better than a single predictor one. It also appears that 7-bit PARCOR coefficients in the MPEG-4 ALS standard have insufficient precision for some predictor length, and that for very long frames coding results improve with the predictor rank practically in unlimited way

Wykorzystanie adaptacyjnego kodera arytmetycznego z przełączaniem kontekstów do bezstratnej kompresji obrazów

Tyt. z nagłówka.Bibliogr. s.117-118Dostępny również w formie drukowanej.STRESZCZENIE: W artykule zaprezentowano metodę bezstratnej kompresji obrazów. Przedstawiono klasyfikację współczesnych metod, a następnie scharakteryzowano podstawowe typy predykcyjnego kodowania. Zaproponowano efektywną metodę mieszania predyktorów wraz z wykorzystaniem kontekstowej korekcji błędu, jako efektywny sposób wstępnego modelowania obrazów. Opracowano też wielokontekstowy, adaptacyjny koder arytmetyczny, który poddano szczegółowej analizie. Połączenie obu propozycji zaowocowało otrzymaniem wydajnej i szybkiej metody kompresji obrazów, którą można w łatwy sposób zaimplementować zarówno sprzętowo, jak i programowo. SŁOWA KLUCZOWE: bezstratna kompresja obrazów, modelowanie predykcyjne, adaptacyjny koder arytmetyczny. ABSTRACT: A method of lossless image compression is described in this paper. A classification of contemporary methods is provided, and basic types of predictive coding are characterized. An effective method of blending predictors together with an application of a context error correction is proposed as an effective way for a first-stage image modeling. The developed, multi-context, adaptive arithmetic encoder is analyzed in details. The combination of the two proposals resulted in obtaining an effective and fast image compression method, which is easily implementable in both software and hardware. KEYWORDS: lossless image compression, predictive modeling, adaptive arithmetic encoder

Lossless Image Coding Using Non-MMSE Algorithms to Calculate Linear Prediction Coefficients

This paper presents a lossless image compression method with a fast decoding time and flexible adjustment of coder parameters affecting its implementation complexity. A comparison of several approaches for computing non-MMSE prediction coefficients with different levels of complexity was made. The data modeling stage of the proposed codec was based on linear (calculated by the non-MMSE method) and non-linear (complemented by a context-dependent constant component removal block) predictions. Prediction error coding uses a two-stage compression: an adaptive Golomb code and a binary arithmetic code. The proposed solution results in 30% shorter decoding times and a lower bit average than competing solutions (by 7.9% relative to the popular JPEG-LS codec)

A High Efficiency Multistage Coder for Lossless Audio Compression using OLS+ and CDCCR Method

In this paper, the improvement of the cascaded prediction method was presented. Three types of main predictor block with different levels of complexity were compared, including two complex prediction methods with backward adaptation, i.e., extension Active Level Classification Model (ALCM+) and extended Ordinary Least Square (OLS+). Our own approach to implementation of the effective context-dependent constant component removal block is also presented. Additionally, the improved adaptive arithmetic coder with short, medium and long-term adaptation was presented, and the experiment was carried out comparing the results with other known lossless audio coders against which our method obtained the best efficiency