• Title/Summary/Keyword: Gamma-tone Energy

Search Result 2, Processing Time 0.015 seconds

Gamma Correction for Local Brightness and Detail Enhancement of HDR Images (HDR 영상의 지역적 밝기 및 디테일 향상을 위한 감마 보정 기법)

  • Lee, Seung-Yun;Ha, Ho-Gun;Song, Kun-Woen;Ha, Yeong-Ho
    • Journal of Korea Multimedia Society
    • /
    • v.19 no.5
    • /
    • pp.837-847
    • /
    • 2016
  • Tone mapping for High Dynamic Range(HDR) image provides matching human visual perception between real world scene and displayable devices. Recently, a tone mapping algorithm based on localized gamma correction is proposed. This algorithm is using human visual properties of contrast and colorfulness with background intensity, generating a weight map for gamma correction. However, this method have limitations of controlling enhancement region as well as generating halo artifacts caused by the weight map construction. To overcome aforementioned limitations, proposed algorithm in this paper modifies previous weight map, considering base layer intensity of input luminance channel. By determining enhancement region locally and globally based on base layer intensity, gamma values are corrected accordingly. Therefore, proposed algorithm selectively enhances local brightness and controls strength of edges. Subjective evaluation using z-score shows that our proposed algorithm outperforms the conventional methods.

Speech Recognition Performance Improvement using Gamma-tone Feature Extraction Acoustic Model (감마톤 특징 추출 음향 모델을 이용한 음성 인식 성능 향상)

  • Ahn, Chan-Shik;Choi, Ki-Ho
    • Journal of Digital Convergence
    • /
    • v.11 no.7
    • /
    • pp.209-214
    • /
    • 2013
  • Improve the recognition performance of speech recognition systems as a method for recognizing human listening skills were incorporated into the system. In noisy environments by separating the speech signal and noise, select the desired speech signal. but In terms of practical performance of speech recognition systems are factors. According to recognized environmental changes due to noise speech detection is not accurate and learning model does not match. In this paper, to improve the speech recognition feature extraction using gamma tone and learning model using acoustic model was proposed. The proposed method the feature extraction using auditory scene analysis for human auditory perception was reflected In the process of learning models for recognition. For performance evaluation in noisy environments, -10dB, -5dB noise in the signal was performed to remove 3.12dB, 2.04dB SNR improvement in performance was confirmed.