Scalable rate control for MPEG-4 video

Lee, Hung-Ju; Chiang, Tihao; Zhang, Yaqin

doi:10.1109/76.867926

Cited by 344 publications

(22 citation statements)

References 18 publications

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“…For framebased video coding standards, e.g., MPEG-1/2 and H.263, the rate control algorithm usually involves two major steps [7][8][9][10]: one at the frame level and the other at the macroblock (MB) level. An additional object-level rate control is necessary for object-based coding standard like MPEG-4 [5,[11][12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Convex Optimization-Based Bit Allocation for Video Coding

Chan

et al. 2009

J Sign Process Syst Sign Image Video Technol

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This paper presents a new convex optimizationbased bit allocation framework for video coding. Motivated by the statistical analysis of the relationship between the rate and the corresponding distortion in typical video sequences, an improved empirical rate-distortion model with more flexibility in explaining the experimental observations is proposed. The convexity and monotonicity properties of the proposed model allow us to formulate the bit allocation problem as a convex programming problem, which can be solved using interior-point methods. Coupled with the region of interest (ROI) functionality and the technique of MB classification, the proposed bit allocation scheme is then applied to the problem of frame-level bit allocation for various video coding standards employing motion compensated hybrid DCT/DPCM technique. Similar bit allocation scheme is also developed for MPEG-4 video object coding at the object level. The relevant model parameters are determined using previously encoded data by means of linear regression. Simulation results show that the proposed algorithms can achieve considerably better PSNR performance than the conventional approaches for the tested video sequences, at the expenses of slight increase in coding complexity.

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Section: Introductionmentioning

confidence: 99%

Convex Optimization-Based Bit Allocation for Video Coding

Chan

et al. 2009

J Sign Process Syst Sign Image Video Technol

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“…When the computed target bit is positive and the number of allocated bits for texture is greater than the minimum bound using (11), then QP is computed using the quadratic rate-distortion model [18]. To maintain smoothness of visual quality, QP is limited to within ±2 of the current value between pictures.…”

Section: Positive Target Bitsmentioning

confidence: 99%

FMO-based H.264 frame layer rate control for low bit rate video transmission

Cajote

Aramvith

Miyanaga

2011

EURASIP J. Adv. Signal Process.

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The use of flexible macroblock ordering (FMO) in H.264/AVC improves error resiliency at the expense of reduced coding efficiency with added overhead bits for slice headers and signalling. The trade-off is most severe at low bit rates, where header bits occupy a significant portion of the total bit budget. To better manage the rate and improve coding efficiency, we propose enhancements to the H.264/AVC frame layer rate control, which take into consideration the effects of using FMO for video transmission. In this article, we propose a new header bits model, an enhanced frame complexity measure, a bit allocation and a quantization parameter adjustment scheme. Simulation results show that the proposed improvements achieve better visual quality compared with the JM 9.2 frame layer rate control with FMO enabled using a different number of slice groups. Using FMO as an error resilient tool with better rate management is suitable in applications that have limited bandwidth and in error prone environments such as video transmission for mobile terminals.

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“…As the target-bit has the positive value, the QP can be computed by using the quadratic R-D model [7] as follows …”

Section: Positive Target-bit (T Bi 0)mentioning

confidence: 99%

“…Also when both the buffer status and the proposed complexity measure are high, the QP is increased by one because it is expected that the target-bit will be negative for the next frame. After encoding a frame, a linear regression method like [7] is used in order to update the parameters of linear prediction model for MAD, as well as x 1 and x 2 of quadratic R-D model (8) for the next frame. The generated-bit is also added to the current buffer fullness.…”

Section: Positive Target-bit (T Bi 0)mentioning

confidence: 99%

Cost-Effective Frame-Layer H.264 Rate Control for Low Bit Rate Video

Lee

et al. 2006

2006 IEEE International Conference on Multimedia and Expo

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The rate control is important to allocate bits efficiently for getting better performance, such as high quality, low fluctuation of quality and low mismatch between a target bit rate and an encoded bit rate. In this paper, we present an improved frame-layer H.264/AVC rate control scheme using enhanced complexity measure, scene-change detection, and quantization parameter adjustment for low bit rate video. Especially, our complexity measure contains not only the residual information but also the motion information. We use the motion vector difference encoded previously as motion information for low computation-load encoder. Experimental results show that Y-PSNR and bit-rate mismatch are remarkably improved and the fluctuation of Y-PSNR is similar as compared with the conventional H.264/AVC rate control scheme (JVT-G012).

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Scalable rate control for MPEG-4 video

Cited by 344 publications

References 18 publications

Convex Optimization-Based Bit Allocation for Video Coding

Convex Optimization-Based Bit Allocation for Video Coding

FMO-based H.264 frame layer rate control for low bit rate video transmission

Cost-Effective Frame-Layer H.264 Rate Control for Low Bit Rate Video

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