On rate distortion optimization using SSIM

Yeo, Chuohao; Tan, Hui Li; Tan, Yaosheng

doi:10.1109/icassp.2012.6288013

Cited by 18 publications

(27 citation statements)

References 8 publications

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“…In this paper, the selection of λ is quite similar compared to [13], which wants to keep the overall rate of encoding one frame to be the same. In H.264, the RD model for each macroblock (MB) is:…”

Section: λ Selectionmentioning

confidence: 98%

“…In [13], the author simply modeled the relationship between the reconstructed pixel y and the original pixel x by an additive distortion model, i.e. y = x + e, where e is the reconstruction error due to the lossy quantization.…”

Section: The Relationship Between Ssim and Msementioning

confidence: 99%

“…Mai et al proposed a SSIM-based RDO framework for Intra coding of H.264 [10] and then extended their work to fast Intra mode decision [11] and motion estimation [12]. Instead of using (1-SSIM) as the distortion measurement, 1/SSIM was also utilized as the distortion measurement in the RDO framework [13].…”

Section: Introductionmentioning

confidence: 99%

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SSIM-based rate-distortion optimization in H.264

Dai

Zhu

et al. 2014

2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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In the current video coding standards, rate-distortion optimization (RDO) plays an important role in achieving best tradeoff between the perceived distortion and transmission rate. It is widely used in all kinds of encoder decisions, including block mode decision, motion vector selection and so on. Generally, the sum of absolute difference (SAD) or the sum of square difference (SSD) is used as the distortion measurement. However, it is well known that both of them cannot always re ect the perceptual quality of the encoded video. In this paper, an objective quality measurement structural similarity (SSIM) index is proposed as the distortion measurement in the RDO framework for video coding standards. By fully exploiting the relationship between SSIM and mean square error (MSE), the SSIM-based RDO framework can be approximated by the original SSD-based RDO framework with only a scaling of the Lagrange multiplier. Experimental results show that the proposed method outperforms the latest H.264 codec and also the state-of-the-art SSIM-based RDO video codec.

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Section: λ Selectionmentioning

confidence: 98%

Section: The Relationship Between Ssim and Msementioning

confidence: 99%

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SSIM-based rate-distortion optimization in H.264

Dai

Zhu

et al. 2014

2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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“…For optimization convenience, we will use (8) in our SSIM computation. Also for convenience, we will define and use distortion of SSIM (dSSIM) instead of quality SSIM during optimization, as done in [6]:…”

Section: Structural Similaritymentioning

confidence: 99%

“…RELATED WORK As new quality metrics are still actively being investigated and proposed [2], [1], RD-optimized coding tailored specifically for an individual metric remains a popular research topic [5], [6]. Our current work is unique in that a general RD-optimization strategy is first sought, so that subsequent re-targeting for a specific metric only requires minimum investment in time and effort.…”

Section: Introductionmentioning

confidence: 99%

Quality-optimized encoding of JPEG images using transform domain sparsification

Ishida

Cheung

Kubota

et al. 2012

2012 IEEE 14th International Workshop on Multimedia Signal Processing (MMSP)

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Abstract-To account for the unique characteristics and limitations of the human visual system (HVS) when perceiving images, a variety of perceptual quality metrics have been proposed in the literature. Tailoring rate-distortion (RD) optimization for each metric is cumbersome and time-consuming. In this paper, we propose a general RD-optimization strategy called "transform domain bounding box" (BB) that can easily adapt to different quality metrics for JPEG-like block-based encoding of images. First, we define an objective function that is a weighted sum of the l0-norm of the transform coefficients (a proxy for rate) and distortion from the transform domain representation. Next, for a given distortion target τ , we define a don't care region (DCR) that specifies a search region of representations with distortion ≤ τ . We then show that the sparsest transform domain representation (lowest encoding rate) inside a BB that tightly contains the DCR can be constructed efficiently. Varying τ to induce different DCRs and corresponding BBs results in a set of constructed sparse representations of different sparsity counts, and the one that optimally trades off rate and distortion can be easily identified as solution to our objective. We show that our proposed BB strategy can be easily re-targeted for three common quality metrics: MSE, MSE-HVS-M and SSIM. Experimental results show that our BB strategy outperformed unoptimized JPEG compression by up to 1dB in PSNR when distortion metric is MSE, up to 2dB when metric is MSE-HVS-M, and up to 0.005 when metric is SSIM.

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Perceptual Based Content Adaptive L 0 Smoothing

Kou

Chen

et al. 2013

Lecture Notes in Computer Science

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On rate distortion optimization using SSIM

Cited by 18 publications

References 8 publications

SSIM-based rate-distortion optimization in H.264

SSIM-based rate-distortion optimization in H.264

Quality-optimized encoding of JPEG images using transform domain sparsification

Perceptual Based Content Adaptive L 0 Smoothing

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